{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "977d952c-72dc-45aa-b6cf-9867bcf6c7cf",
   "metadata": {},
   "source": [
    "# Statistics of KuaiRec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "896097a5-b4f8-40e2-8280-6da14b15c163",
   "metadata": {},
   "source": [
    "We provide some basic statistics of the KuaiRec dataset. The description of this dataset can be referred to https://chongminggao.github.io/KuaiRec/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c7a222-89d1-429a-9f7b-f6a2af0a816a",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d21b590-73c5-4608-b797-ee4e78c6d034",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you are running locally, make sure you are in the directory of KuaiRec.\n",
    "rootpath=\"./\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0b3633-30a3-4585-b49c-705f70dc2ed9",
   "metadata": {},
   "source": [
    "If you are using Google Colab, make sure you have added shortcut of this [shared link](https://drive.google.com/drive/folders/1bAm07YnKRKB6SVHB8Mqz6v6W8ppLaJwB) to your own Google Drive. Then, you should load it from your space by indicating the correct path as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "12c0a1e9-d3cd-4c0e-bfc4-f795e4fc9344",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')\n",
    "\n",
    "# rootpath=\"./drive/MyDrive/Datasets/KuaiRec/\" # Make sure this path corresponds to KuaiRec in your Drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4dcc656-e666-4177-9e9a-49904bddd86f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading big matrix...\n",
      "Loading small matrix...\n",
      "Loading social network...\n",
      "Loading item features...\n",
      "Loading user features...\n",
      "Loading items' daily features...\n",
      "All data loaded.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "print(\"Loading big matrix...\")\n",
    "big_matrix = pd.read_csv(rootpath + \"data/big_matrix.csv\")\n",
    "print(\"Loading small matrix...\")\n",
    "small_matrix = pd.read_csv(rootpath + \"data/small_matrix.csv\")\n",
    "\n",
    "print(\"Loading social network...\")\n",
    "social_network = pd.read_csv(rootpath + \"data/social_network.csv\")\n",
    "social_network[\"friend_list\"] = social_network[\"friend_list\"].map(eval)\n",
    "\n",
    "print(\"Loading item features...\")\n",
    "item_categories = pd.read_csv(rootpath + \"data/item_categories.csv\")\n",
    "item_categories[\"feat\"] = item_categories[\"feat\"].map(eval)\n",
    "\n",
    "print(\"Loading user features...\")\n",
    "user_features = pd.read_csv(\"data/user_features.csv\")\n",
    "\n",
    "print(\"Loading items' daily features...\")\n",
    "item_daily_features = pd.read_csv(\"data/item_daily_features.csv\")\n",
    "\n",
    "print(\"All data loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f881a995-6ed5-4a04-8b47-4606ef7e54ec",
   "metadata": {},
   "source": [
    "## Visualization of the four tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c945ef7-6771-4b00-8ab8-87195bb77c19",
   "metadata": {},
   "outputs": [
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       "          user_id  video_id  play_duration  video_duration  \\\n",
       "0               0      3649          13838           10867   \n",
       "1               0      9598          13665           10984   \n",
       "2               0      5262            851            7908   \n",
       "3               0      1963            862            9590   \n",
       "4               0      8234            858           11000   \n",
       "...           ...       ...            ...             ...   \n",
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       "\n",
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       "4         2020-07-05 00:43:05.128  20200705  1.593881e+09     0.078000  \n",
       "...                           ...       ...           ...          ...  \n",
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       "\n",
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   "outputs": [
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       "         user_id  video_id  play_duration  video_duration  \\\n",
       "0             14       148           4381            6067   \n",
       "1             14       183          11635            6100   \n",
       "2             14      3649          22422           10867   \n",
       "3             14      5262           4479            7908   \n",
       "4             14      8234           4602           11000   \n",
       "...          ...       ...            ...             ...   \n",
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       "3        2020-07-05 05:30:43.285  20200705.0  1.593898e+09     0.566388  \n",
       "4        2020-07-05 05:35:43.459  20200705.0  1.593899e+09     0.418364  \n",
       "...                          ...         ...           ...          ...  \n",
       "4676565                      NaN         NaN           NaN     2.178160  \n",
       "4676566                      NaN         NaN           NaN     1.964562  \n",
       "4676567                      NaN         NaN           NaN     0.839960  \n",
       "4676568                      NaN         NaN           NaN     0.486148  \n",
       "4676569                      NaN         NaN           NaN     0.112666  \n",
       "\n",
       "[4676570 rows x 8 columns]"
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     "execution_count": 5,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb713ad2-49f2-46e4-a14f-828a434d0082",
   "metadata": {},
   "outputs": [
    {
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       "      <td>[26]</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>[5]</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10723</th>\n",
       "      <td>10723</td>\n",
       "      <td>[11]</td>\n",
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       "    <tr>\n",
       "      <th>10724</th>\n",
       "      <td>10724</td>\n",
       "      <td>[2]</td>\n",
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       "    <tr>\n",
       "      <th>10725</th>\n",
       "      <td>10725</td>\n",
       "      <td>[15]</td>\n",
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       "    <tr>\n",
       "      <th>10726</th>\n",
       "      <td>10726</td>\n",
       "      <td>[19]</td>\n",
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       "    <tr>\n",
       "      <th>10727</th>\n",
       "      <td>10727</td>\n",
       "      <td>[5]</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>10728 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       video_id     feat\n",
       "0             0      [8]\n",
       "1             1  [27, 9]\n",
       "2             2      [9]\n",
       "3             3     [26]\n",
       "4             4      [5]\n",
       "...         ...      ...\n",
       "10723     10723     [11]\n",
       "10724     10724      [2]\n",
       "10725     10725     [15]\n",
       "10726     10726     [19]\n",
       "10727     10727      [5]\n",
       "\n",
       "[10728 rows x 2 columns]"
      ]
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     "execution_count": 6,
     "metadata": {},
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   "id": "f8768e9c-3599-4666-888e-619919ca5191",
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       "     user_id   friend_list\n",
       "0       3371        [2975]\n",
       "1         24        [2665]\n",
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       "3       4295        [4694]\n",
       "4       7087        [7117]\n",
       "..       ...           ...\n",
       "467     2331        [4345]\n",
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       "\n",
       "[472 rows x 2 columns]"
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       "      <td>2020-09-05</td>\n",
       "      <td>ShortImport</td>\n",
       "      <td>public</td>\n",
       "      <td>5132.0</td>\n",
       "      <td>528</td>\n",
       "      <td>960</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>10727</td>\n",
       "      <td>20200905</td>\n",
       "      <td>7464</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>2020-09-05</td>\n",
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       "      <td>public</td>\n",
       "      <td>5666.0</td>\n",
       "      <td>720</td>\n",
       "      <td>1556</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>343341 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        video_id      date  author_id video_type   upload_dt  upload_type  \\\n",
       "0              0  20200705       3309     NORMAL  2020-03-30  ShortImport   \n",
       "1              0  20200706       3309     NORMAL  2020-03-30  ShortImport   \n",
       "2              0  20200707       3309     NORMAL  2020-03-30  ShortImport   \n",
       "3              0  20200708       3309     NORMAL  2020-03-30  ShortImport   \n",
       "4              0  20200709       3309     NORMAL  2020-03-30  ShortImport   \n",
       "...          ...       ...        ...        ...         ...          ...   \n",
       "343336     10723  20200905        236     NORMAL  2020-09-05  ShortImport   \n",
       "343337     10724  20200905       5271     NORMAL  2020-09-05   LongImport   \n",
       "343338     10725  20200905       1924     NORMAL  2020-09-05  ShortImport   \n",
       "343339     10726  20200905       7604     NORMAL  2020-09-05  ShortImport   \n",
       "343340     10727  20200905       7464     NORMAL  2020-09-05  ShortCamera   \n",
       "\n",
       "       visible_status  video_duration  video_width  video_height  ...  \\\n",
       "0              public          5966.0          720          1280  ...   \n",
       "1              public          5966.0          720          1280  ...   \n",
       "2              public          5966.0          720          1280  ...   \n",
       "3              public          5966.0          720          1280  ...   \n",
       "4              public          5966.0          720          1280  ...   \n",
       "...               ...             ...          ...           ...  ...   \n",
       "343336         public          4833.0          720          1280  ...   \n",
       "343337         public         54720.0          720          1280  ...   \n",
       "343338         public         15800.0          576          1024  ...   \n",
       "343339         public          5132.0          528           960  ...   \n",
       "343340         public          5666.0          720          1556  ...   \n",
       "\n",
       "        download_cnt  download_user_num report_cnt  report_user_num  \\\n",
       "0                  8                  8          0                0   \n",
       "1                  2                  2          0                0   \n",
       "2                  2                  2          0                0   \n",
       "3                  3                  3          0                0   \n",
       "4                  2                  2          2                1   \n",
       "...              ...                ...        ...              ...   \n",
       "343336             0                  0          0                0   \n",
       "343337             1                  1          0                0   \n",
       "343338             5                  5          0                0   \n",
       "343339             2                  2          0                0   \n",
       "343340             0                  0          0                0   \n",
       "\n",
       "        reduce_similar_cnt  reduce_similar_user_num  collect_cnt  \\\n",
       "0                        3                        3          NaN   \n",
       "1                        5                        5          NaN   \n",
       "2                        0                        0          NaN   \n",
       "3                        3                        3          NaN   \n",
       "4                        1                        1          NaN   \n",
       "...                    ...                      ...          ...   \n",
       "343336                   0                        0          0.0   \n",
       "343337                   0                        0          0.0   \n",
       "343338                   4                        4          0.0   \n",
       "343339                   1                        1          0.0   \n",
       "343340                   0                        0          0.0   \n",
       "\n",
       "        collect_user_num  cancel_collect_cnt  cancel_collect_user_num  \n",
       "0                    NaN                 NaN                      NaN  \n",
       "1                    NaN                 NaN                      NaN  \n",
       "2                    NaN                 NaN                      NaN  \n",
       "3                    NaN                 NaN                      NaN  \n",
       "4                    NaN                 NaN                      NaN  \n",
       "...                  ...                 ...                      ...  \n",
       "343336               0.0                 0.0                      0.0  \n",
       "343337               0.0                 0.0                      0.0  \n",
       "343338               0.0                 0.0                      0.0  \n",
       "343339               0.0                 0.0                      0.0  \n",
       "343340               0.0                 0.0                      0.0  \n",
       "\n",
       "[343341 rows x 58 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_daily_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "035f85e7-a904-48b5-94fd-29221e14ecae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_active_degree</th>\n",
       "      <th>is_lowactive_period</th>\n",
       "      <th>is_live_streamer</th>\n",
       "      <th>is_video_author</th>\n",
       "      <th>follow_user_num</th>\n",
       "      <th>follow_user_num_range</th>\n",
       "      <th>fans_user_num</th>\n",
       "      <th>fans_user_num_range</th>\n",
       "      <th>friend_user_num</th>\n",
       "      <th>...</th>\n",
       "      <th>onehot_feat8</th>\n",
       "      <th>onehot_feat9</th>\n",
       "      <th>onehot_feat10</th>\n",
       "      <th>onehot_feat11</th>\n",
       "      <th>onehot_feat12</th>\n",
       "      <th>onehot_feat13</th>\n",
       "      <th>onehot_feat14</th>\n",
       "      <th>onehot_feat15</th>\n",
       "      <th>onehot_feat16</th>\n",
       "      <th>onehot_feat17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>high_active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>(0,10]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>184</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>full_active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>386</td>\n",
       "      <td>(250,500]</td>\n",
       "      <td>4</td>\n",
       "      <td>[1,10)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>186</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>full_active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>(10,50]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>full_active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>(10,50]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>251</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>full_active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>(100,150]</td>\n",
       "      <td>4</td>\n",
       "      <td>[1,10)</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7171</th>\n",
       "      <td>7171</td>\n",
       "      <td>full_active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>(50,100]</td>\n",
       "      <td>1</td>\n",
       "      <td>[1,10)</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>259</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7172</th>\n",
       "      <td>7172</td>\n",
       "      <td>full_active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>(10,50]</td>\n",
       "      <td>2</td>\n",
       "      <td>[1,10)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7173</th>\n",
       "      <td>7173</td>\n",
       "      <td>full_active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>615</td>\n",
       "      <td>500+</td>\n",
       "      <td>3</td>\n",
       "      <td>[1,10)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7174</th>\n",
       "      <td>7174</td>\n",
       "      <td>full_active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>959</td>\n",
       "      <td>500+</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7175</th>\n",
       "      <td>7175</td>\n",
       "      <td>full_active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>(100,150]</td>\n",
       "      <td>35</td>\n",
       "      <td>[10,100)</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>132</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7176 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id user_active_degree  is_lowactive_period  is_live_streamer  \\\n",
       "0           0        high_active                    0                 0   \n",
       "1           1        full_active                    0                 0   \n",
       "2           2        full_active                    0                 0   \n",
       "3           3        full_active                    0                 0   \n",
       "4           4        full_active                    0                 0   \n",
       "...       ...                ...                  ...               ...   \n",
       "7171     7171        full_active                    0                 0   \n",
       "7172     7172        full_active                    0                 0   \n",
       "7173     7173        full_active                    0                 0   \n",
       "7174     7174        full_active                    0                 0   \n",
       "7175     7175        full_active                    0                 0   \n",
       "\n",
       "      is_video_author  follow_user_num follow_user_num_range  fans_user_num  \\\n",
       "0                   0                5                (0,10]              0   \n",
       "1                   0              386             (250,500]              4   \n",
       "2                   0               27               (10,50]              0   \n",
       "3                   0               16               (10,50]              0   \n",
       "4                   0              122             (100,150]              4   \n",
       "...               ...              ...                   ...            ...   \n",
       "7171                1               52              (50,100]              1   \n",
       "7172                0               45               (10,50]              2   \n",
       "7173                0              615                  500+              3   \n",
       "7174                0              959                  500+              0   \n",
       "7175                1               98             (100,150]             35   \n",
       "\n",
       "     fans_user_num_range  friend_user_num  ... onehot_feat8  onehot_feat9  \\\n",
       "0                      0                0  ...          184             6   \n",
       "1                 [1,10)                2  ...          186             6   \n",
       "2                      0                0  ...           51             2   \n",
       "3                      0                0  ...          251             3   \n",
       "4                 [1,10)                0  ...           99             4   \n",
       "...                  ...              ...  ...          ...           ...   \n",
       "7171              [1,10)                0  ...          259             1   \n",
       "7172              [1,10)                2  ...           11             2   \n",
       "7173              [1,10)                2  ...           51             2   \n",
       "7174                   0                0  ...          107             3   \n",
       "7175            [10,100)               33  ...          132             5   \n",
       "\n",
       "     onehot_feat10  onehot_feat11  onehot_feat12  onehot_feat13  \\\n",
       "0                3              0            0.0            0.0   \n",
       "1                2              0            0.0            0.0   \n",
       "2                3              0            0.0            0.0   \n",
       "3                2              0            0.0            0.0   \n",
       "4                2              0            0.0            0.0   \n",
       "...            ...            ...            ...            ...   \n",
       "7171             4              0            1.0            0.0   \n",
       "7172             0              0            1.0            0.0   \n",
       "7173             2              0            1.0            0.0   \n",
       "7174             2              0            0.0            0.0   \n",
       "7175             2              0            0.0            0.0   \n",
       "\n",
       "      onehot_feat14  onehot_feat15  onehot_feat16  onehot_feat17  \n",
       "0               0.0            0.0            0.0            0.0  \n",
       "1               0.0            0.0            0.0            0.0  \n",
       "2               0.0            0.0            0.0            0.0  \n",
       "3               0.0            0.0            0.0            0.0  \n",
       "4               0.0            0.0            0.0            0.0  \n",
       "...             ...            ...            ...            ...  \n",
       "7171            0.0            0.0            0.0            0.0  \n",
       "7172            0.0            0.0            0.0            0.0  \n",
       "7173            0.0            0.0            0.0            0.0  \n",
       "7174            0.0            0.0            0.0            0.0  \n",
       "7175            0.0            0.0            0.0            0.0  \n",
       "\n",
       "[7176 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d8c1e4-6419-4e89-a628-58f389049b5e",
   "metadata": {},
   "source": [
    "## Codes for visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "427696e2-1ff7-488f-930b-419f98c2dd37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "myfont = FontProperties(fname=\"./SimHei.ttf\")\n",
    "\n",
    "def visual_continue(df, func=None):\n",
    "    ax = sns.distplot(df)\n",
    "    if func:\n",
    "        func(ax)\n",
    "    \n",
    "    gca = plt.gca()\n",
    "    fig_title = \"Statistics of {}\".format(df.name)\n",
    "    gca.set_title(fig_title, fontsize=14)\n",
    "    gca.set_ylabel(\"Density\", fontsize=14)\n",
    "    gca.set_xlabel(df.name, fontsize=14)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "def visual_statistics_discrete(df, var=\"my_variable\", display_ratio=True, func=None, order=None, size=(6, 4.5)):\n",
    "    ncount = len(df)\n",
    "\n",
    "    fig = plt.figure(figsize=size)\n",
    "    ax1 = fig.add_axes([0.14, 0.15, 0.74, 0.75])\n",
    "    sns.countplot(x=df, color=\"#9fc5e8\", linewidth=.6, edgecolor='k', ax=ax1, order=order)\n",
    "\n",
    "\n",
    "    plt.grid(axis='y', linestyle='-.')\n",
    "\n",
    "    gca = plt.gca()\n",
    "    fig_title = \"Statistics of {}\".format(var)\n",
    "    gca.set_title(fig_title, fontsize=14)\n",
    "    gca.set_ylabel(\"Count\", fontsize=14)\n",
    "    gca.set_xlabel(var, fontsize=14)\n",
    "    \n",
    "    if func:\n",
    "        func(ax1)\n",
    "\n",
    "    if display_ratio:\n",
    "        # Make twin axis\n",
    "        ax2 = ax1.twinx()\n",
    "        ax2.set_ylabel(\"ratio (%)\", fontsize=14)\n",
    "\n",
    "\n",
    "        for p in ax1.patches:\n",
    "            x = p.get_bbox().get_points()[:, 0]\n",
    "            y = p.get_bbox().get_points()[1, 1]\n",
    "            ax1.annotate('{:.1f}%'.format(100. * y / ncount), (x.mean(), y),\n",
    "                         ha='center', va='bottom', fontsize=10, rotation=30)  # set the alignment of the text\n",
    "\n",
    "        ax2.set_ylim(0, ax1.get_ylim()[1] / ncount * 100)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ccf7276-a1a4-4cdc-a63d-8cdbdc262586",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "754ffa8c-9513-4c06-a33b-219a9bdf351a",
   "metadata": {},
   "source": [
    "## Statistics of social network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9deed902-2d29-427d-9946-ccd4626a9379",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    472.000000\n",
      "mean       1.419492\n",
      "std        0.853295\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        2.000000\n",
      "max        5.000000\n",
      "Name: friend_list, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(social_network.friend_list.map(len).describe())\n",
    "visual_statistics_discrete(social_network.friend_list.map(len), \"number of friends\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349b8abc-e28a-4c56-a6bb-954c3c418df8",
   "metadata": {},
   "source": [
    "## Statistics of video features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9156d479-294b-4c54-8105-d5ea1da01a9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    10728.000000\n",
      "mean         1.183166\n",
      "std          0.436205\n",
      "min          1.000000\n",
      "25%          1.000000\n",
      "50%          1.000000\n",
      "75%          1.000000\n",
      "max          4.000000\n",
      "Name: feat, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_feat = item_categories.feat.map(len)\n",
    "print(num_feat.describe())\n",
    "visual_statistics_discrete(num_feat, \"number of tags\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a79853-4894-4913-ad96-1692f8f54e18",
   "metadata": {},
   "source": [
    "## Distribution of the 31 tags of items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02591918-680e-48fa-8390-4fc1721fd84f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RcI/t8j1Yp6HJ3/53seoKnLF1c1kO9BeRhiLSEOhv26aUUkoppVS1VJl9yrsDdwO/2+aABPgn8DLW6WZGA38Cw21lS7BOh7gf65SI9wEYY5JFZBqQP2fki8aY5Eq5B0oppZRSSlWASkvKbX3DpYjifoXsb4BHiqhrLtaO+EoppZRSSlV7uryeUkoppZRSTqZJuVJKKaWUUk6mSblSSimllFJOpkm5UkoppZRSTqZJuVJKKaWUUk6mSblSSimllFJOpkm5UkoppZRSTqZJuVJKKaWUUk6mSblSSimllFJOpkm5UkoppZRSTqZJuVJKKaWUUk6mSblSSimllFJOpkm5UkoppZRSTqZJuVJKKaWUUk6mSblSSimllFJOpkm5UkoppZRSTqZJuVJKKaWUqnVEZK6InBCRXYWUTRARIyKNKyseTcqVUkoppVRt9DFw4/kbRaQl0B84XJnBaFKulFJKKaVqHWPMOiC5kKLXgacBU5nxVFpSXthPBCLytYjssP0dEpEdtu2BIpJRoOzdAsd0EZHfRWS/iLwpIlJZ90EppZRSStVcIjIQSDDGRFf2bbtV4m19DLwFfJK/wRgzIv+yiLwGnCmw/wFjTKdC6nkHeBDYDCzB+rPD0vIPVymllFJKVWNuIrK1wPU5xpg5Re0sInWBf2LtulLpKi0pN8asE5HAwspsrd3DgeuKq0NEmgG+xphfbdc/AQahSblSSimllHKUa4yJKMX+rYEgINrWESMAiBKRa4wxxysiwIKqSp/ynkCiMWZfgW1BIrJdRNaKSE/bthZAfIF94m3blFJKKaWUumTGmN+NMU2MMYHGmECseWZ4ZSTkULndV4ozEviywPVjwOXGmCQR6QL8T0RCS1upiIwBxgC4u7uzZs0ah/KmTZsSFBREbGwsoaGhrFu37oI6unXrRlxcHAEBASQmJnLkyBGH8hYtWhAQEEBcXBwhISFs2LDhgjp69OhBbGwsQUFBxMfHk5CQ4FDesmVL/P39iY+PJygoiE2bNl1QR69evYiJiSEkJIS4uDiOH3d8fQQGBuLn50diYiIBAQFs3rz5/MeC3r17Ex0dTWhoKLGxsZw4ccJhn1atWuHj40NycjL+/v5s3brVodzNzY0ePXoQFRVFp06diImJISkpyWGf4OBgPD09OXv2LH5+fkRFRTmUe3p60q1bN7Zt20aXLl2Ijo4mJSXFYZ+2bdvi6upKZmYmPj4+REc7duvy8vIiMjLSXkdUVBSpqakO+7Rv3568vDwsFguenp7s2uU425G3tzcRERH2OrZu3crZs2cd9unQoQNZWVm4uLjg6urK7t27Hcp9fX0JDw+317F582YyMjIc9gkLCyMtLY06deqQl5fH3r17HcobNmxIWFiYvY5NmzaRlZXlsE94eDjJycl4e3uTlZXFvn37HMobNWpEaGgoO3bsIDw8nA0bNpCbm+uwT0REBImJifj5+ZGWlsbBgwcdyps0aUJISAgxMTGEhYWxdu1ajHEc3xIZGUl8fDz+/v4kJydz6NAhh/LSnk/tglvjkRLrUJ7n2YA8zwa4ZSSRW68JHqcPXFBHdoPWuJ07QV0vL/bt26fnk55PdrX5fNLPJz2fCtLz6RAlISJfAn2AxiISDzxvjPmwRAdXADn/jlXojVm7ryw2xnQosM0NSAC6GGPiizhuDTDRtt9qY0w72/aRQB9jzNiL3Xa9evXMuXPnynwflFLl4/ZR9zBs3AuXfPyCt57nmy/mlWNESimlahIRSTfG1HN2HCVVFbqvXA/sKZiQi8hlIuJqu9wKCAYOGmOOAaki0tXWD/3vwEJnBK2UUkoppVR5qcwpEb8ENgFtRSReREbbiu7AsesKQC9gp22KxG+Afxhj8ueRfBj4ANgPHEAHeSqllFJKqWquMmdfGVnE9nsL2fYt8G0R+28FOhRWppRSSimlVHVUFbqvKKWUUkopVatpUq6UUkoppZSTaVKulFJKKaWUk2lSrpRSSimllJNpUq6UUkoppZSTaVKulFJKKaWUk2lSrpRSSimllJNpUq6UUkoppZSTaVKulFJKKaWUk2lSrpRSSimllJNpUq6UUkoppZSTaVKulFJKKaWUk2lSrpRSSimllJNpUq6UUkoppZSTaVKulFJKKaWUk2lSrpRSSimllJNpUq6UUkoppZSTaVKulFJKKaWKde7cObZt2+bsMMqViMwVkRMisqvAtldFZI+I7BSR70WkQWXFo0m5UkoppZQq1meffcavv/4KQF5enpOjKTcfAzeet20l0MEYcxUQC0yurGA0KVdKKaWUUhfYsGEDu3ZZG5HbtGnDiRMnAHB1dXVmWOXGGLMOSD5v2wpjTK7t6q9AQGXFU2lJeRE/EUwVkQQR2WH7G1CgbLKI7BeRvSLytwLbb7Rt2y8ikyorfqWUUkqp2iAzM5N77rmHF154gWeffZYjR45w/Phx2rVrB0BsbCy///67k6OsFPcDSyvrxtwq64aw/kTwFvDJedtfN8bMLLhBRNoDdwChQHPgJxEJsRW/DdwAxANbRGSRMWZ3RQaulFJKKVXTnTlzhj///JOrrroKd3d3vv32W5YuXconn3xC//79+fbbb2ncuDEffvghfn5+REREcP/992OMQUScHX5h3ERka4Hrc4wxc0pyoIg8C+QCn1dIZIWotJbywn4iKMZA4CtjTJYxJg7YD1xj+9tvjDlojMkGvrLtq5RSSimlLtEbb7xBs2bNmDnT2k7auXNnpk2bRlxcHNnZ2ezatYvIyEiMMfTr14/Zs2fz1Vdfce7cuaqakAPkGmMiCvyVNCG/F7gFuNMYYyo0wgKqQp/ycbYRrnNFpKFtWwvgSIF94m3bitqulFJKKaVKaePGjXTu3Jk9e/bw/vvvEx0dTW5uLv/4xz8IDAzk4MGDTJo0iTZt2vDbb7/h7+/Ppk2b+PjjjxERjh496uy7UK5E5EbgaeA2Y0x6Zd52ZXZfKcw7wDTA2P6/hrX/TrkQkTHAGAB3d3fWrFnjUN60aVOCgoKIjY0lNDSUdevWXVBHt27diIuLIyAggMTERI4cOeJQ3qJFCwICAoiLiyMkJIQNGzZcUEePHj2IjY0lKCiI+Ph4EhISHMpbtmyJv78/8fHxBAUFsWnTpgvq6NWrFzExMYSEhBAXF8fx48cdygMDA/Hz8yMxMZGAgAA2b958/mNB7969iY6OJjQ0lNjYWPuAjXytWrXCx8eH5ORk/P392bp1q0O5m5sbPXr0ICoqik6dOhETE0NSUpLDPsHBwXh6enL27Fn8/PyIiopyKPf09KRbt25s27aNLl26EB0dTUpKisM+bdu2xdXVlczMTHx8fIiOjnYo9/LyIjIy0l5HVFQUqampDvu0b9+evLw8LBYLnp6e9oEq+by9vYmIiLDXsXXrVs6ePeuwT4cOHcjKysLFxQVXV1d273bsJeXr60t4eLi9js2bN5ORkeGwT1hYGGlpadSpU4e8vDz27t3rUN6wYUPCwsLsdWzatImsrCyHfcLDw0lOTsbb25usrCz27dvnUN6oUSNCQ0PZsWMH4eHhbNiwgdzcXId9IiIiSExMxM/Pj7S0NA4ePOhQ3qRJE0JCQoiJiSEsLIy1a9dyfuNAZGQk8fHx+Pv7k5yczKFDhxzKS3s+tQtujUdKrEN5nmcD8jwb4JaRRG69JnicPnBBHdkNWuN27gR1vbzYt2+fnk96PtnV5vNJP5/0fCqopOdTSkoKf/75J8888wxNmzblzJkz+Pv789FHH3HNNdfwyCOPsG3bNry8vFi7di0Wi4WUlBTatGnDJ598wmOPPYa7uzunTp2q0udTUUTkS6AP0FhE4oHnsc624gmstP0C8Ksx5h8lqrCMpBJb5RGRQGCxMaZDcWUiMhnAGPNvW9lyYKpt16nGmL/ZtjvsV5x69eqZc+fOlcO9UEqVh9tH3cOwcS9c8vEL3nqeb76YV44RKaVUzXaxvt9Hjhxh5MiRzJ07l5CQEPLy8nBxcUFEWL16NT/++KO9e0t1ICLpxph6zo6jpJzafUVEmhW4OhjI/7q4CLhDRDxFJAgIBn4DtgDBIhIkIh5YB4MuqsyYlVJKKaWqo4Kt5ec3ylosFlq2bEmTJk349NNPAevUh/lJ/IkTJ7j11lsLPVaVj0rrvlLETwR9RKQT1u4rh4CxAMaYGBGZD+zGOvL1EWNMnq2eccBywBWYa4yJqaz7oJRSSilV3axatYrnn3+e4OBg+vXrx1133YWIOLSc5/8fMGAAhw8fJjs7Gw8PD/s+Q4YMwd3d3WFfVb4qLSk3xowsZPOHxew/HZheyPYlwJJyDE0ppZRSqkZKTk7mX//6FxMmTKBRo0bMmjWLuLg4nnvuOSwWi30hoPxEOzc3lz179uDh4YHFYsHFxdqpIj8hVxXH2QM9lVJKKaVUObJYLAC4uLhw9OhROnbsyODBg3F1dSUgIIDIyEgeeOABmjVrRk5ODu7u7uTl5eHq6soNN9zAK6+8wunTp2nQoIFz70gtUxWmRFRKKaWUUuXgo48+IiAggClTpgDW2Vw2bdrEqVOnAOssNKNGjWLcuHHAXy3g2dnZALRu3ZqYmBhNyJ1Ak3KllFJKqRrg7NmzLFy4kGeeeYYlS5awd+9eAgMDCQ8PZ/z48fb9ZsyYQXx8vH0Kw7fffpvPP/9r4cq6detWdugK7b6ilFJKKVUjeHt78+abb3L55Zdz7NgxpkyZwtdff83s2bMJDAxk06ZNdOvWjbp16xIWFoaXlxfGGEaMGEHjxo2dHX6tpy3lSimllFI1xOWXXw7A+PHjiYuL48cff6RevXpMnTqVl156iY8++ojp06ezc+dO6tati4hoQl5FaFKulFJKKVXDNG3alNGjR/Pyyy8D8MgjjzBu3Dj++OMPYmNjWbBgAX5+fk6OUhWk3VeUUkoppWoYi8XC2LFjWblyJePGjcPFxYUHHniAV155RecZr6K0pVwppZRSqoZxcXEhPT2dEydO8PXXX9OmTRuuuuoqTcirMG0pV0oppZSqgWbPnk14eDgrV67E09PT2eGoi9CkXCmllFKqBnryySftK3Kqqk+fKaWUUkqpGkgT8upFW8qVUkoppWqBsQ8/StLp1DLV0aiBL+/N/m85RaQK0qRcKaWUUqoWSDqdyrBxL5SpjgVvPV9O0ajz6e8aSimllFJKOZkm5UoppZRSSjmZJuVKKaWUUko5mfYpV0oppZRS6hKISANgCNAbCAS8gJNAFLDUGPNLSevSlnKllFJKKVXriMhcETkhIrsKbPMTkZUiss/2v2ERxzYXkQ+AY8CzgAewFVgB/Ik1SV8pIrtFZERJ4tGWcqWUUkopVRt9DLwFfFJg2yRglTHmZRGZZLv+TCHH7gDmARHGmJjCKhcRL2AQ8KSItDTGzCwumEprKS/i28irIrJHRHaKyPe2nwAQkUARyRCRHba/dwsc00VEfheR/SLypohIZd0Hpaoai8Xi7BCUUkqpaskYsw5IPm/zQKzJNrb/g4o4PNQY81RRCbmt/gxjzJfGmMgCdRapMruvfAzceN62lUAHY8xVQCwwuUDZAWNMJ9vfPwpsfwd4EAi2/Z1fp1I1ljGGpKQkpk2bBuhqbUoppVQ58zfGHLNdPg74F7aTMeZkaSotyf6V9ole2LcRY8wKY0yu7eqvQEBxdYhIM8DXGPOrMcZg/blhUAWEq1SVJCK4urpy5swZcnJytKVcKaWUKpqbiGwt8DemNAfbck1T0v1FxFtEXhGRLSISZevR4VfiYEsTXAW7H/i6wPUgEdkOpAL/MsasB1oA8QX2ibdtU6pGmzlzJu3atSMyMpKjR49y5swZ3N3dnR2WUkopVZXlGmMiSnlMoog0M8YcszUGnyjFse9iTeKfxzrw8yHgU+DmkhxcJZJyEXkWyAU+t206BlxujEkSkS7A/0Qk9BLqHQOMAXB3d2fNmjUO5U2bNiUoKIjY2FhCQ0NZt27dBXV069aNuLg4AgICSExM5MiRIw7lLVq0ICAggLi4OEJCQtiwYcMFdfTo0YPY2FiCgoKIj48nISHBobxly5b4+/sTHx9PUFAQmzZtuqCOXr16ERMTQ0hICHFxcRw/ftyhPDAwED8/PxITEwkICGDz5s3nPxb07t2b6OhoQkNDiY2N5cQJx9dZq1at8PHxITk5GX9/f7Zu3epQ7ubmRo8ePYiKiqJTp07ExMSQlJTksE9wcDCenp6cPXsWPz8/oqKiHMo9PT3p1q0b27Zto0uXLkRHR5OSkuKwT9u2bXF1dSUzMxMfHx+io6Mdyr28vIiMjLTXERUVRWpqqsM+7du3Jy8vD4vFgqenJ7t27XIo9/b2JiIiwl7H1q1bOXv2rMM+HTp0ICsrCxcXF1xdXdm9e7dDua+vL+Hh4fY6Nm/eTEZGhsM+YWFhpKWlUadOHfLy8ti7d69DecOGDQkLC7PXsWnTJrKyshz2Wb9+PUuWLOH222/nm2++YciQIWRkZLBmzRp+++03LBYLw4cP5/Tp04SHh7NhwwZyc3Md6oiIiCAxMRE/Pz/S0tI4ePCgQ3mTJk0ICQkhJiaGsLAw1q5di7WB4C+RkZHEx8fj7+9PcnIyhw4dcigv7fnULrg1HimxDuV5ng3I82yAW0YSufWa4HH6wAV1ZDdojdu5E9T18mLfvn16Pun5ZFeS8yk8PJzk5GS8vb3Jyspi3759DuWNGjUiNDSUHTt2VKvzST+f9HwqqKjzacANfe3vuzneAUheJri4g7Hglp7oUIfFrS65PgG4p/5Jju8VuJ8+iJhcBtzQ155PVZfzqZQWAfcAL9v+LyxqRxF5wBjzQYFN3YBgY4zFVr4b2FLSG5bz71hFEpFAYLExpkOBbfcCY4F+xpj0Io5bA0wEEoDVxph2tu0jgT7GmLEXu+169eqZc+fOlfUuKFUpEhIS8Pb2pn79+kyYMIHx48fTsmVLXn75Zdq3b8+SJUv497//zbhx4+jbty8xMTHcfvvtdO/e3dmhl9jto+5h2LgXLvn4BW89zzdfXHTcjFJKKZuyvu9C9XrvFZF0Y0y9Ysq/BPoAjYFErC3c/wPmA5djndpwuDHm/MGg+cd/Yzv2QWPMPhH5HMi2He8O/APAGDOgJPE6taVcRG4EngZ6F0zIReQyINkYkycirbAO6DxojEkWkVQR6QpsBv4O/NcZsStVETIyMpgyZQo//vgjbdq0YcyYMfTo0YOZM2eSnJxMRkYGDz74IBs3bmTZsmX07NmTBx54gA8//JD09EK/0yqllFKqEMaYkUUU9Svh8beLyCBgqYjMBR7FOmnJS1jHbW4AppY0nhIn5SLSC/ilwMDM/O1uwLW2gZzFHW//NiIi8Vi/jUwGPLFOrg7wq22mlV7AiyKSA1iAfxT4lvIw1plcvICltj+laoQZM2Zw7NgxYmJiWLRoETNnzmTNmjV06NCBn3/+mb///e/8+eefZGZmctNNN3H33Xdz4sQJPvzwQ77//ntnh6+UUkrVKsaY/4nIz1i7u6wGxhpjnrqUukrTUr4aKKzDe31bmWtxBxfxbeTDIvb9Fvi2iLKtQIfCypSqzrKysjh37hwDBw5ERDh58iR9+/bl6NGjBAcHExwcDFj7iYoIDRo04I033uC3335j4cKFdOrUybl3QCmllKqFjDGpwMMici0wx9bterIxplT9pkszJaJQ+LQwjQDtrK1UKZ0/xsHT0xM/Pz+WL19O9+7dmT59OqmpqURGRjoMSNqzZw+nTp0CoHXr1owcOVITcqWUUqqSicjlIjLftqjl50Ac0AU4BewQkVtKU99FW8pFZJHtogE+E5GCQ9ldsbZa/1KaG1WqNsvNzWXKlCls3bqVYcOG0b9/f6644goAJk2axMmTJ3n88cdZsmQJ9evXp2HDhkycOJGff/4ZgC5dujBggHXMiDEGXdRWKaWUcopPsC4w9BTwN+A9Y8xtWLtgzwfeE5F7jDHDSlJZSVrKk2x/AqQUuJ6EdZ7wd4G7SnsvlKqNUlJSGDVqFKdPn+aJJ57g+++/t0/rZozBzc2NU6dO4efnh7e3NwBjx47F09OTtLQ0wLqK56hRowA0IVdKKaWcJwJ41hizDHgSuCq/wBizxxjTG+vq9SVy0ZZyY8x9ACJyCJhZ2v4xSqm/nD17lkOHDjF//nwAFixYYC/LT7A7dOjAxo0bmTVrFkFBQbz55ptce+21+Pj4OCVmpZRSShVqG9ZW8XnA9cDv5+9gjJlT0spKPNDTGFO2iS2VUrRs2ZK6dety7733Eh8fz6FDh0hKSmLXrl3ccccdNG/eHBFh9uzZrFu3jlmzZjF27FhGjixq1iallFJKOcnfgdeA14EdWNfduWQlHugpIn4i8o6IxIrIadt84fa/sgShVG2yYMECrr32Wpo3b87+/fuZMGECx48fZ/HixQDExMRw7bXX8uyzz7JmzRpNyJVSSqkqyBjzpzHmdmNMqDHmTmPM0bLUV5opET8EOgNzgKMUPhOLUuoiLrvsMjw8PGjcuDFgXaJ68eLF1KlTh++//57Tp08TEhKCq2uxs4wqpZRSyklExMcYk1ae+5cmKe8H3GCM2VyKY5RShWjTpg1Llizh119/pVWrVmzatImuXbsyePBgZ4emlFJKqYvbJyJvAR8bY+IL20FEXLDOyvIksArrAkNFKk1SfgI4W4r9laowFouF1NRU3n33XZ566qlq16ocERFBt27d+Oc//8mJEyd47LHHGDJkiLPDUkoppVTJ9ASmAwdF5HdgK9aeJJlAQ6A90BXIAGYA71+swtIk5c9iHWF6jzFGk3PlVC4uLjRo0ICdO3dy+PBhgoKCnB1SqdSpU4cnnniCQYMGERAQgLu7u7NDUkoppVQJGWP2AcNFpCUwHGuSfg3ghXXxoO1Yu3wvMcZYSlJnaZLyfwGBwAkR+RPIOS+4qwo7SKnyNGPGDDw9PenevTtdu3blsssuq3YJeUHVOXallFKqtjPGHME6A8trZa2rNEn5N2W9MaXK4ssvv2TJkiVMmTKFcePGsXXrVpKSkjh69Ch79+4lKiqKjh070r9/f2eHWipjH36UpNOXPoFRowa+vDf7v+UYkVJKKaUqm85Trqq03bt3s3DhQiZPnoynpydjxoyhf//+rFq1iqVLlxIUFIS3tzdz587lnnvu4d133yUlJYURI0ZUmyXok06nMmzcpZ9eC956vhyjUUoppZQzlHiecqUqkzGGZ555hhEjRpCTY+0p1bhxY44ePcqQIUP47bff6NWrF7t27WLLli106tSJ66+/nkceeYS4uDhAl6BXSimlVPVR4pZyEUmjmLnJjTG+5RKRUkBSUhLx8fFs2bKFOnXqANb5vHv06MEPP/xAnz59qFu3LmFhYXTs2JGoqCjGjh3LsmXLmD179iXdZnWf0UUppZRS1Vdp+pSPO++6O9bFhIZinRJGqTLL73Jy9uxZ9u7dS506dfjpp5/Yvn077du35+abb2bgwIH2/Xft2kVmZiZPPfUU69evZ9y4cXTs2PGSbru6z+iilFJKqeqrNH3K5xW2XUSisC4spCPN1CU5evQosbGx9OnTx97lJDc3lz59+vD888+zfv16hgwZwlNPPUViYiLDhg3Dx8eHM2fO0KRJE5o1awZAz549L+n2a9qMLkoppZSqXCLiCdyJdX5yA8QAXxpjskpaR3n0KV8N3FoO9ahaJi8vj+eee45WrVoxf/58wJqMAzRp0oTc3FzWrVvHq6++yrhx45g+fTqLFy/GYrFO91m/fn369etXpjm+58+fz5IlS+jYsSPjxll/DMqf0WX16tW89tprrFixooz3VCmllFI1lYi0B2KB/wCRWBcNegOIFZErS1pPeSTld2CdJF2pElu8eDEdOnTAGMO7777Lli1bAHBzc8NiseDr68stt9yCl5cXUVFRAAwePJiUlBTS0tLs9QwdOrTUt33ixAl7Yu/m5sYDDzxA//796dev3wUzuoSFhTFnzhy+/vprwNq9RimllFI1g4g8ISIxIrJLRL4UkTqXUM0sYAdwuTGmpzGmJ3A5EI01OS+R0gz0/B3HgZ4C+AN+wEMlrUcpAB8fHz7++GMiIyM5d+4c3333HVu2bOHqq6+2d2G5/vrrOXXqFN988w0nT55kxYoVXHnllTRq1OiSbjM9PZ1//etfrF69mquvvprBgwcTHh7Op59+ypAhQ0hJSWHKlCnMmTPHYUYXV1dXNm/eDOiMLkoppVRNISItgMeA9saYDBGZj7Wx+eNSVtUduNoYY190xBiTKiLPAr+WtJKyLB5kAU4Ca4wxe0pSgYjMBW4BThhjOti2+QFfY10t9BAw3BiTItbsZxYwAEgH7jXGRNmOuQfrCqMALxXV311VXb1797ZfPnbsGC4uLvj6WifwKTi/+B133MFVV13FsmXLGDNmDKNGjbqk2zt69CiPPvoozZs3Z8mSJaxbt47HHnuM2NhYnnvuORYuXFghM7oopZRSqkpzA7xEJAeoCxy9hDoygQaFbK9vKytxICVSTosHfQy8BXxSYNskYJUx5mURmWS7/gxwExBs+4sE3gEibUn880AE1pb7bSKyyBiTUg7xKSdo06YNZ86cYdGiRTz11FO4uFh7VW3cuJHw8HDat29P+/bty3QbXl5e3Hfffdxyyy0ADBs2jG+++YZt27YRERFRYTO6KKWUUqpqMsYkiMhM4DCQAawwxlzKQLIfgPdF5EH+ahnvBrwHLCppJaXuUy4i14nIOBF5RET6lOZYY8w6IPm8zQOB/JbuecCgAts/MVa/Ag1EpBnwN2ClMSbZloivBG4s7f1QVUNeXh4Ad999N4cOHbIP9FyzZg379++/5O4iR48eZc2aNfbrDRs2pG/fvvbr8fHx7N+/3yHZz8vLIzU19YIZXTQhV0oppaolNxHZWuBvTMFCEWmINd8MApoD9UTkrku4nceBfcB6rC3jmcBarIM/x5c42JLuaOt38z3Qhb+a9puLyFZgsDHmUpr7AfyNMcdsl49j7acO0AI4UmC/eNu2oraraih/gR4RIScnBzc360uyV69e9hbz0sjLy2Pq1Km8+uqr3H///fTp04e8vDxcXV2pV6+efb/s7GyuuOIK6tat6xCLr69vmWd0UUoppVSVkGuMiSim/HogzhhzEkBEvgOuBT4rzY0YY04DA0UkGGhn2/yHMWZ/aeopTZ/yN4E8oI0xJg5ARFphDfxN4PbS3HBhjDFGRMptegvbN6IxAO7u7g4tpwBNmzYlKCiI2NhYQkNDWbdu3QV1dOvWjbi4OAICAkhMTOTIkSMO5S1atCAgIIC4uDhCQkLYsGHDBXX06NGD2NhYgoKCiI+PJyEhwaG8ZcuW+Pv7Ex8fT1BQEJs2bbqgjl69ehETE0NISAhxcXEcP37coTwwMBA/Pz8SExMJCAiwD0ws8FjQu3dvoqOjCQ0NJTY2lhMnTjjs06pVK3x8fEhOTsbf35+tW7c6lLu5udGjRw+ioqLo1KkTMTExJCUlOewTHByMp6cnZ8+exc/Pzz5zSj5PT0+6devGtm3b6NKlC9HR0aSkpJCTk8OPP/7IypUr6dChA66urmRmZuLj40N0dLRDHV5eXkRGRtrriIqKIjU1lU2bNvHuu+/Sq1cv/u///o+5c+eSkJCAp6cnu3btcqjjt99+IygoiG3btrF9+3a8vLxo0cL63a5Ro0asWbOGDh06kJWVhYuLC66uruzevduhDl9fX8LDw+1xbN68mYyMDId9wsLCSEtLo06dOuTl5bF3716H8oYNGwLgnvonOb5X4H76IGJyHfbJ8WmJS046FldPxJKDW8ZJh/KITh2xWCzs2LGD8PBwNmzYYP/Fwb5PRASJiYn4+fmRlpbGwYMHHcqbNGlCSEgIMTExhIWFsXbt2gtmmomMjCQ+Ph5/f3+Sk5M5dOiQQ3lpz6d2wa3xSIl1KM/zbECeZwPcMpLIrdcEj9MHLqgju0Fr3M6doK6XF/v27dPzqZDzqaC2bdte0vlUUPv27cnLy8NisRR6Pnl7exMREWGvY+vWrZw9e9Zhn8o6n8LCwux1bNq0iawsxymCw8PDSU5Oxtvbm6ysLPbt2+dQ3qhRI0JDQ6vd+aSfT3o+FVTU+TTghr72990c7wAkLxNc3MFYcEtPdKjD4laXXJ+ACz6fBtzQ155PVZfzqRiHga4iUhdr95V+wNbiDymaMWYf1hbzSyIlneJNRFKBPvmDLQtsj8DaJ7x+CesJBBYXGOi511bvMVv3lDXGmLYi8p7t8pcF98v/M8aMtW132K8o9erVM+fOnSvRfVWVK39gZ25urr2l/FKsXbuWOnXq2Gd0GTlyJM899xxXX331Bfs+8sgjnD17loyMDBITE3nnnXfK3G/9Ut0+6h6Gjbv0IRsL3nqeb76ofmOda+v9VkopZynr+y5Ur/deEUk3xtS7yD4vACOAXGA78EBJFvwRkTeBycaYc7bLRTLGPFaSeEubARWWwZe1ZXsRcA/wsu3/wgLbx4nIV1gHep6xJe7LgRm2fkAA/YHJZYxBOVF+v/GyJORQ/IwuFovFoTvM4cOHOXDgAFOnTmX48OFlul2llFJKVU/GmOexTiBSWh0B9wKXy6w0WdAq4L8iMtIYcwRARC7HOin6qpJUICJfYm3pbiwi8VgfhJeB+SIyGvgTyM+QlmCdDnE/1ikR7wMwxiSLyDRgi22/F40x5w8eVbVccTO6dO/enQkTJtCnTx/nBqmUUkqpaskY07ewy2VRmqT8Mayt1wdFxD7QE/gdGFmSCowxRe3Xr5B9DfBIEfXMBeaW5DZV9TH24UdJOp168R2L0KiBL+/N/q99YOfdd9/Ntm3b7N1i1qxZQ1xcHFdffbUm5EoppZQqFyIyBZhpjEk/b7sX8JQx5sWS1FOaecqPiEg41pGqBUeW/lTSOpQqTtLp1DL3MYbiZ3TRZFwppZRS5ex54F2sPTsKqmsrK1FSftE550TkJhE5JCK+tjnDVxpj/muM+S+wxVZ2Q2mjV6qihYWFsWrVKrKzswEuaYpFpZRSSqmLEAofY9mZC9fnKVJJWsrHAa8aYy7oV2CMOSMir2CdGH1lSW9UqYpmjCE8PJy9e/eWeQCpUkoppdT5RCQNazJusHbvLpiYuwJ1sLagl0hJspWrgCeLKf8ZeLakN6hUZSivGV2UUkoppYowDmsr+VysufCZAmXZwCFjzIWT+xehJBnLZYClmHIDNCrpDSqllFJKKVXdGWPmAYhIHPCLMSanLPWVJCmPx9paXtQKRVcBCUWUKeU0ZZ3NBf6a0UUppZRSqjDGmLX5l0WkKeBxXvnhktRTkqT8R2CaiCwxxjiseWxblvRF2z5KVSllnc0F/prRRSmllFKqMCLiC/wX61o7HoXs4lqSekoyHcV0oD4QKyLPiMhA298kINZWNqNkYSullFJKKVWjvAaEAYOATGAU8BTW3iYjSlrJRVvKjTEnRORa4B2sybfkFwHLgUeMMYmliVwppZRSSqka4iZgpDFmvYjkAduMMV+LyDFgLPBNSSop0dQUxpg/gQEi0hBogzUx32eMSbm02JVSSimllKoRGgB/2i6fwToByn5gE/BBSSsp1WoqxpgUY8wWY8xvmpArVTNYLBZOnz7Nyy+/TF5enrPDUapaMqawdUOUUrXEAaCV7fIfwB1inZt5CKVYPEiXOFSqlnNxcaFBgwbs3LmTw4dLNEBcqVrPYrGQnJzMiy9aV8/OXxtBKVUrfYx1NkKAl7F2WckGXgVeKWklurKKUrXUjBkz8PT0pHv37nTt2pXLLruMoKAgZ4elVLXg4uJCw4YNOXz4MElJSTRqpMt1KFVbGWNeL3D5ZxFpB0Rg7er9e0nr0ZZypWqhr776iiVLltCxY0fGjRsHQFJSEkePHmX16tW89tprrFixwslRKlX1/Pvf/2bevHn88ccfnDx5EhcXF03IlarFRMRdRDaLSNv8bcaYw8aY70qTkIMm5UrVGvv37+fHH61LCnh4eDBmzBj69+9Pv379WLp0KUFBQXh7ezN37lzCwsKYM2cOX3/9NaD9ZZUCmDNnDkuWLMHb25vx48fTsGFDRIT09HRWrFjBe++9x7Zt25wdplKqEtlW8QzCOithmWhSrlQNl56ezlNPPcWwYcPIzMwE4LLLLuPo0aMMGTKE3377jV69erFr1y62bNlCp06duP7663nkkUeIi4sDtL+sqr3279/Pn39aJ1XIyMhg+vTpDB06lMjISJYvX07dunWxWCzMmzcPf39/Zs2axaJFi5wctVKqks0DHixrJdqnXKkaLC0tjREjRpCYmMj27dvt23v27Em3bt1YvHgxffv2pW7duoSFhdGxY0eioqIYO3Ysy5YtY/bs2U6MXinnOnPmDO3bt2fChAlMmzaNwMBAli9fzttvv01qaiqTJk3i22+/ZfXq1VxzzTUMGjQIESEpKcnZoSulKlc94E4RuQHYBpwrWGiMeawklWhSrlQNVqdOHe68805WrlwJwK+//srp06cJDAykXbt2DBo0yL7vrl27yMzM5KmnnmL9+vWMGzeOjh07OilypZwvISGB4OBgMjIyWLduHQMHDqRXr178+OOPDBw4kPT0dBo3bkyvXr1Yvnw5EydOZP78+Xz22WfODl0pVbmuBKJsl1udV1bibi2alCtVg5w8eZLLLrvMft3d3Z2+ffuydu1amjZtSnBwMF27duXzzz9n6dKlhIWFkZ2dTWZmJk2aNKFZs2aAtSVdqdquQYMG3H777aSnp7N8+XJCQ0Px9/fnrrvuAqBu3bqcOHECgGnTpvHLL79w9913ExYW5sywlVKlICINsC7w0wFrAn2/MWZTaeowxvQtj1g0KVeqBsjLy+OFF17gyy+/5KeffuKKK66wlzVr1oxRo0YRHBzMU089BUD9+vWZMGECP/30Ex4eHnh4eNCvXz/c3d2ddReUcprzv8zm2759O2lpabzyyis8/PDDLFiwgObNm3PTTTfh5eXFkSNHEBHq168PwM0331zZoSulym4WsMwYc7uIeAB1nRWI0wd6ikhbEdlR4C9VRMaLyFQRSSiwfUCBYyaLyH4R2Ssif3Nm/Eo526kTiQQHB5OWlsb69esdEnJjDCJCt27d7Ak5wKhRo/Dx8SEjIwOLxQLA0KFDKz12pZwpLy+PKVOmcO2119oHcxbUoUMHAgICcHd3Jy4ujscff5zdu3fj5eUFQGBgIP3796/ssJVS5URE6gO9gA8BjDHZxpjTzorH6Um5MWavMaaTMaYT0AVIB763Fb+eX2aMWQIgIu2BO4BQ4EZgtoi4OiF0paoEN3d30tLSeP3112natClxcXGcPn0a+GvWFE9PT/v+v/32G/fffz9XX301Xl5euLg4/W1AqUq3fv36Ir/M5tu0aRNz586lY8eOXHbZZYwcOZIrrriCc+f+GsM1atSoygxbKVW+goCTwEcisl1EPhCRes4Kpqp1X+kHHDDG/FnMFGwDga+MMVlAnIjsB64BStX/R6maokFDPwYPHszw4cNp2LAhe/fuxdPTkzFjxnD99dfbf1o/d+4c77zzDp999hnPPPMMI0eOdHLkSjmPr6+v/cssQFxcHA0bNqRBgwb2fUaMGMHKlSsZPXo01157LQsXLmTXrl06b79S1YebiGwtcH2OMWZOwXIgHHjUGLNZRGYBk4DnKjPIgsFUJXcAXxa4Pk5E/g5sBSYYY1KAFsCvBfaJt227gIiMAcaAdcDbmjVrHMqbNm1KUFAQsbGxhIaGsm7dugvq6NatG3FxcQQEBJCYmMiRI0ccylu0aEFAQABxcXGEhISwYcOGC+ro0aMHsbGxBAUFER8fT0JCgkN5y5Yt8ff3Jz4+nqCgIDZtuvD7Ra9evYiJiSEkJIS4uDiOHz/uUB4YGIifnx+JiYkEBASwefPm8x8LevfuTXR0NKGhocTGxtoHKOVr1aoVPj4+JCcn4+/vz9atWx3K3dzc6NGjB1FRUXTq1ImYmJgLpv4KDg7G09OTs2fP4ufnR1RUlEO5p6cn3bp1Y9u2bXTp0oXo6GhSUlIAGHBDXzxSYsmt6w/iApYcjGsd3M/GO9RhXNzJqR+Ee+qf5PhegXvqYSQvkwE39LU/x+3btyegeTNcMlMwLu64nzvqWIerp/3YHN/LcU/9E8nLctgnp15zrmjZgmPHjuHq6sru3bsdyn19fQkPD7ffl82bN5ORkeGwT1hYGGlpadSpU4e8vDz27t3rUN6wYUOAv+7L6YOIyXWMw6clLjnpWFw9EUsObhknHcojOnVk7IOj6dy5M/fddx933303P/zwA1988QV//vkn4eHh5OTkEBgYSO/evRk+fDgHDx50OB+aNGlCSEgIMTExhIWFsXbt2gsSj8jISOLj4/H39yc5OZlDhw45lJf2fGoX3BqPlFiH8jzPBuR5NsAtI4ncek3wOH3ggjqyG7TG7dwJ6np5sW/fPj2fijif8rVt2xZXV1cyMzPx8fEhOjraodzLy4vIyEh7HVFRUaSmpjrs0759e/Ly8rBYLHh6erJr1y6Hcm9vbyIiIux1bN26lbNnzzrs06FDB7KysnBxcanQ8yksLMxex6ZNm8jKcjyvw8PDSU5OpkWLFvTv358+ffrg4+PDkSNHcHd3Z+TIkdx3333ExcURHh7OfffdR3Z2NmvWrMHX15fu3bsDcODAAfz8/EhLS+PgwYMOt+GM80k/n/R8Kqio8yn/cxYgxzsAycsEF3cwFtzSEx3qsLjVJdcn4ILPp4Kftfnnk7e3N1lZWezbt8+hjkaNGhEaGsqOHTsIDw9nw4YN5OY6fsZFRESQmJhYUedTrjEmgqLFA/HGmPwX5TdYk3KnkKryjd/Wuf4oEGqMSRQRf+AU1pGw04Bmxpj7ReQt4FdjzGe24z4Elhpjvimu/nr16pmCPzmqquf2UfcwbNwLl3z8gree55sv5pVbfYXVWRHK634fP36cpk2b2rcPGDCAxx57jPr167N7927uuusuh24szlbez7dSpXXmzBkiIyMZOXIkzz//PB999BE7duxg4MCBXHfddWzcuJGIiIgqdd4oVRbV5XOxvIhIujGm2O4oIrIeeMAYs1dEpgL1jDFPFXdMRalKLeU3AVHGmESA/P8AIvI+sNh2NQFoWeC4ANs2pWq1ggn5gQMHyM7OpkmTJoSHh9OtWzcnRqZU1VS/fn3WrFljP3fuu+8+BgwYQHZ2Nps2bSI2NpaIiOIa2ZRSNcCjwOe2xuGDwH3OCqQqJeUjKdB1RUSaGWOO2a4OBvJ/31kEfCEi/wGaA8HAb5UZqFJVkTGG5ORknnjiCXbv3s2YMWMIDw93dlhKVWn6ZVap2s0YswOoEt++q0RSbhvpegMwtsDm/xORTli7rxzKLzPGxIjIfGA3kAs8YozJq9SAlaqCRARPT0+6d+/O+++/rz+5l0L+1JGq9tEvs0qpqqJKJOXGmHNAo/O23V3M/tOB6RUdl1LVjbe3N2PHjr34jrWcxWLh9OnTvPXWW0yZMkUT8lpMv8wqpaqKKpGUK6XKz9iHHyXpdOrFdyxGowa+vDf7v+UUUdXj4uJCw4YNOXz4MElJSTRq1OjiB6kaS7/MKqWqAk3Klaphkk6nlsvo+pro3//+N82bNycyMhI/Pz9cXFw0IVd2Zf1CW9O/zCqlKpYm5UpVEovFoqtnOtH777/PkiVLGD9+PI8//jiLFy9GREhPT2fDhg3ExcURERFBly5dnB2qcpKyfqGtqV9mlVKVQzMEpSqIMYakpCSmTZsGoAm5ExRcPCQ9PZ2XXnqJoUOHEhkZyfLly6lbty4Wi4V58+bh7+/PrFmz+OGHHwB01UallFKVSrMEpSqIiODq6sqZM2fIycnBYrE4O6Ra49y5c0yYMIEBAwYwefJkoqKi6Ny5M8uWLWPEiBFs2bKF6667jtOnT7N69WquueYaBg0axNChQ+2JvA7+VEpVFm0EUKBJuVLlbubMmSxevJiTJ0/y559/cubMGdzd3bWlvJIcOHCAIUOGYLFY+Oijj2jWrBmTJ0+mV69eTJo0idtuu4358+eTnp5O48aN6dWrF/v27WPixIk8+uijtGrVytl3QSlVw1ksFpKTk3nxxRcBbQRQVtqnXKlyNHPmTH788Ud8fHyYOnUq06ZNo0mTJgAsW7aMpKQkunfvTmBgoHMDrcHq1avH6NGjGT58OACenp5s2bKFQ4cOERgYyJ133mnf78SJEwBMmzaNX375hbvvvpuwsDCnxa6Uqh7KuraBzgClCqNNd06mP1lVf6dOnQKsLR/Hjh3jk08+YezYsbRs2ZINGzaQlJRESkoKn376KRkZGcyaNYuNGzc6OeqaIzXVcbaMpk2bMmDAAPv1zMxM9uzZc8EXocOHDyMi1K9fn4YNG3LzzTdrQq6UKlR5tWz/+9//Zt68eezZs4eTJ0/qDFDKgSbllUx/sqo5srKyGDduHL179+bZZ5/l999/59prr2XmzJncfffdbN26le7du1O/fn2WLVtGz549eeCBB+jQoQPp6enODr9GePvtt+nduzfbtm0DIC/Purivt7e3fZ/k5GSCg4MvODYwMJD+/ftXTqBKqWrt/JbtS5E/A5S3tzePP/44DRs2tM8AtWLFCt577z37e5mqnTQpr2TlcWKrqmHevHkkJCSwbt06goODGTJkCDfddBNPPPEEPXv25NNPP+XkyZNkZmZy00038eOPP/Liiy/y4osvastIGeX/wpSWlkbdunWZM2cOAK6urhfss3v3btq3bw/Al19+yR9//GHfZ9SoUZUVslKqGipry3b+L6mgM0Cpi9OkvJLoT1Y1R8E3ysjISBo1asS9995L165deeaZZwgMDGTMmDF4eXnRokULRIQGDRrwxhtvEBwczMKFCwkPD3fiPaj+RARjDImJifzjH//AGMPnn38OQG5urn0fgI0bN3Lq1Cluv/12PvnkE9zd3Z0Wt1Kq+njnnXcuuWU7IyPDPgPUjBkz2Lp1K1dffTVLly7VGaBUkTQprwTvvvuu/mRVzRXst5z/RpmamkpycrI9SX/ttddYvHgxe/bsse+7Z88ee0tJ69atGTlyJJ06daq8wGsoi8WCiNC4cWPq1atH3759+fHHHzl8+LDDc5Wdnc2uXbtYu3Ytt99+O0uXLqVNmzZOjFwpVZXt3r2bJ598ErC+f0yfPv2SWrZffvllTp06xbJly+jcuTODBw+mc+fOPPvss9x66606A5QqlCblFWTv3r28//77gLXv8aWe2Mr5zu+3nJOTA8DIkSNZunQp0dHRgHWA4bBhw5gxY4b92IiICPugQ31ey0/+9JK///47f/vb37jxxhvZvXs33bt3JzY2FoCYmBg8PDyYMmUK27dv54477nBmyEqpKiw9PZ2nnnqK4cOH8+mnnxIbG0vr1q1ZsWJFqVu2MzMzOX78OA8//DB+fn7cdNNNeHl5MX78eHx8fLjrrrvw8fHBz8/PYQaovn378sMPP9CrVy+nPQ7KuTQpL2cWi4Unn3ySIUOG2FvsmjVrxsqVK/Unq2qmqH7L7u7u5Obm0qJFC4YOHcqrr77K8ePHAejfvz+tWrXCGGNvzc3vt6zPa/kLCwvj4Ycfpk+fPvj6+tKuXTtCQ0P56aef+PXXX7FYLAwaNMjZYSqlqrDNmzdz4403YrFYWLBgAXfeeSenT5/mlltuYfz48QwcOLDYlu2WLVs61FenTh2ysrKYP38+KSkpJCQk0L17d5YsWcK+ffsAazc7nQFKnU+T8nJ28OBBjhw5QkxMDBMmTABg+PDhPPHEE/qTVTVTXL/l/Fk+nn/+eTw9PXnhhRf44IMPePrpp6lfvz4ioosFVQKLxcKJEyd48803WbduHZ06deKjjz7iuuuuY/To0focKKUuqmXLlnz88ce89tprXHnllURHR7Np0yYAGjduzKhRowpt2e7Vqxc33HAD8+fPZ/HixWRlZdnrnDFjBidOnODhhx+mf//+3HPPPdx555188MEHALi5uekMUOoCunhQOTt79qy9hXzlypUcPnyYtm3b0qNHD+666y5AFy2pLgrrt/zDDz/Qs2dPvL298fT0RETsg3g+//xzxo8fz9///ndnh15rPPnkkzz77LOA9ZeNiRMn4u/v7+SolFJVWXZ2Nh4eHvbrzZs3B6xdE93d3bn33ntZsmQJjz32mP0XTmOMQ8v2Tz/9xOTJk+nduzd9+vTh6aef5rPPPiM8PJy8vDyaN2/ORx99xMGDB2nYsCGXXXYZUVFR9ven3Nxc3NzcdAYo5UCbkcogOzv7gm1nzpzhqquu4j//+Q8vvPACKSkpjBgxgqVLl2KMueDE1p+sqq6L9Vs2xhATE0OjRo245ZZb+PLLLzUhr2ReXl6A9QNORDQhV0oVKS8vj3/+8588+uijF7RsA/aZmTw8PPD39ycrK8vejVFEHFq2W7Zsydtvv83s2bMZPnw4HTt2JCMjA7BOzWqMwc3NjeDgYC677DK2bNnC//73Pxo3bgxYW8qVOp8m5ZeguBM7NDSUffv28fPPP/Pxxx8zceJEnn/+eWbNmoWIXHBiq6qvqH7Lq1atYsuWLYAO4nQ2/YBTShXnp59+4qqrruL06dP069ePp59+mpiYGId98t/H27dvz48//oiLi4tDSzn8tbZB27Zt6dOnD6mpqdx0001s2rSJN998k59//pkzZ87Yj8vKyuKDDz7gzjvv5MEHH+Rvf/tbZd1lVQ1pUl5KxZ3YeXl5NG7cmOHDh5OWlsbBgwcBGDNmDNnZ2Q6LBelPVtVHcf2W7733Xtzd3XUQp6o2LBYLp0+f5uWXX7aPjVCqpiuuZTtf/jiisLAwWrVqxffff+9QVhhfX19uu+02Dh8+zNChQ1m0aJG9sSYmJoY6deowYMAAYmNjufvuuyvuDqpLJiKuIrJdRBY7O5Yq07wkIoeANCAPyDXGRIiIH/A1EAgcAoYbY1LEenbMAgYA6cC9xpioyogz/8Tu06cPAN9++639xM7v7jBq1CgOHTrE999/z549e/j222+JiIigfv36lRGiKmfab7l6GPvwoySdTr34jsVo1MCX92b/t5wiqppcXFxo0KABO3fu5PDhwwQFBTk7JKUqXNu2bWnbti2pqamMGDHC3piWlZVFly5d7J/PIkJSUhLXXnvtRbuVGmMQER566CEA+3SKOTk5LFy4kKSkJNq2bWvvs66qrMeBPwBfZwdSZZJym77GmFMFrk8CVhljXhaRSbbrzwA3AcG2v0jgHdv/ClfSE/uJJ55g586dfPPNN4wZM4Y777yzMsJTFaBgv2U3NzdNyKuopNOpDBv3QpnqWPDW8+UUTdUzY8YMPD096d69O127duWyyy6rNgl5fvKjVFnlt2wvXbqU+fPns2jRIiwWC9dffz27du2ibdu2NGrUiBdeuPh7yfmvyYMHD5KRkYG/v7+u2lxNiEgAcDMwHXjSyeFU+e4rA4F5tsvzgEEFtn9irH4FGohIs8oMrLifrHbt2oWbmxuRkZG8+uqrmpDXENpvWVVX8+fPZ8mSJXTs2JFx48YBkJSUxNGjR1m9ejWvvfYaK1ascHKUf7FYLCQnJ/Piiy8COse/Kh/5/cILtmwfOHDA3rK9devWUtdpsVg4cuQI99xzD8OGDWPEiBGakFcvbwBPAxYnxwFUrZZyA6wQEQO8Z4yZA/gbY47Zyo8D+U2ULYAjBY6Nt207RiW42E9WKSkptG3btjJCUZVMu0io6iIxMdH+q46bmxsPPvgg/fv3Z9WqVSxdupSgoCC8vb2ZO3cu99xzD++++659tihnt0y7uLjQsGFDDh8+TFJSEo0aNXJaLKrmqIiWbRcXFzw9PenWrRtz5szB09OzPEJV5cdNRAp+25pjyy8RkVuAE8aYbSLSxxnBna8qJeU9jDEJItIEWCkiewoWGmOMLWEvMREZA4wB61RHa9ascShv2rQpQUFBxMbGEhoayrp16y6oo1u3bsTFxREQEEBiYiJHjhxxKG/RogU5OTmcOnWKRo0akZ6eTv369dm4ceNfd6xHD2JjYwkKCiI+Pp6EhASHOlq2bIm/vz/x8fEEBQXZFy0oqFevXsTExBASEkJcXJx9Bcl8gYGB+Pn5kZiYSEBAAJs3bz7/saB3795ER0cTGhpKbGysfa70fK1atcLHx4fk5GT8/f0vaDVwc3OjR48eREVF0alTJ2JiYhwGrwIEBwfj6enJ2bNn8fPzIyrKsat//pvXtm3b6NKlC9HR0aSkpAAw4Ia+eKTEklvXH8QFLDkY1zq4n413qMO4uJNTPwj31D/J8b0C99TDSF4mA27oa3+O27dvT0DzZrhkpmBc3HE/d9SxDldP+7E5vpfjnvonkuc4PVZOveZc0bIFx44dw9XVld27dxN5dRcaNmluq6OO/dgc3ytwPxOHWHIc6/AOQPIywcUdjAW39ERSThy1x9mwYUOAv+o4fRAxuY51+LTEJScdi6snYsnBLeOkQ3lEp45YLBZ27NhBeHg4/fv2xCMl9rw6rsAlOxWLez0kLxO3jFMO5RZ3H3LrNcHt7DFyfQK46fo+F5wvkZGRxMfH4+/vT3JyMocOHXIoL+351C649QVx5nk2IM+zAW4ZSeTWa4LH6QMX1JHdoDVu505Q18uLffv22c+n/NdPnmdDLB6+uGSlkOfVGI8zBwupIxi3c0fJreuPa8YpXLNT7XWsWbOmWp9PiYmJfPDBB2zbto3OnTszdOhQWrZsya+//sott9xCQkICffpYn98GDRrQsGFDrr/+eg4cOEBcXBxRUVH2tRbytW/fnry8PCwWC56enuzatcuh3Nvbm4iICHscW7du5ezZsw77dOjQgaysLFxcXOznU0G+vr4sX76c7OxsRowYYX+f+/333+37hIWFkZaWRp06dcjLy2Pv3r0OdTRs2JCwsDB7HJs2bbpg2rvw8HCSk5Px9vYmKyvLvsIiWJ9/t7MJ5NZrjnvaEXJ8L8fj9H4wjo1oRZ1P+a+fJk2aEBISQkxMDGFhYaxdu/aCGZrK+3wq6vMpICCAuLg4QkJC2LBhwwV11LbPJ3d3d/bv389bb73F1q1bue2220hNTWXNmjXFfj7la9u2La6urmRmZuLj40N0dDQA7dq1Y9OmTXh5eREZGWmvw5nnU3h4uL2OzZs3k5GRYX+fhMI/nwqyuNUl1yfggs+ngp+1xZ1PAI0aNSI0NNT++bRhwwZycx0/4yIiIkhMTMTPz89hkox8ZTyfco0xERSuO3CbiAwA6gC+IvKZMeauIvavcFIVp3ITkanAWeBBoI8x5pite8oaY0xbEXnPdvlL2/578/crqs569eqZc+fOlVuMFouFhIQE/vWvf7Fr1y7+8Y9/8OCDD5Zb/bXR7aPuKVOf4AVvPc83X8yzXy9rfdWlzsqIsSLU1vtd0d544w02b97Ml19+yYYNG3jooYf49ttvad26NUuWLKFnz57Ur1+fF154gYcffph58+axf/9+li1bxuzZs7n55pudEvecOXP49NNPGT9+PHPmzGHx4sWMGzeO119/nQ0bNhAXF0dERARdunSpsBjK+zWpqqYTJ07w3Xffcd9995Vry3ZZf0mtjF9Ra9v7pIikG2PqlWC/PsBEY8wtFR5UMapES7mI1ANcjDFptsv9gReBRcA9wMu2/wtthywCxonIV1gHeJ4pLiGvCPqTlVKqKslfnCwlJcWeWPfo0QMfHx+effZZ5s6dy6233mrff9euXWRmZvLUU0+xfv16xo0bR8eOHSs15iNHjmCxWLjiiivIyMjgpZdesreYLl++nLp162KxWJg3bx7Dhg1j1qxZDBs2jFtvvdXpXWxU9dWkSRP+8Y9/lHu9ZR1sXpMHmquSqRJJOda+4t/b3mDdgC+MMctEZAswX0RGA38Cw237L8E6HeJ+rFMi3lf5IVfcia2UUiWRl5eHq6srgH1xMmMM27Zto3379gQGBtK2bVtiYmL4448/uOaaa8jOziYzM5MmTZrQrJl1fHzPnj0rNe7MzEyeffZZlixZQtOmTbnvvvu45pprWLRoEbNnzyY1NZVJkybx7bffsnr1aq655hoGDRpkn64u//4qdal0fJAqyBizBljj5DCqRlJujDkIXDAhqDEmCehXyHYDPFIJoV2UnthKqcqWl5fHc889R15eHtdffz033HCDveyxxx5j1qxZvPzyy8TGxvLSSy8RFBTEO++8wzXXXIOHhwceHh7069fPvqx4ZXv77bc5fPgwf/zxBxs3bmT06NHs2bOHtm3bsmTJEgYOHEh6ejqNGzemV69eLF++nIkTJzJ//nw+++wzp8SsahadQlVVRVUiKa/O9MRWSlWmtWvX8vjjj9OtWzciIiJ49tln8fT0pFevXlgsFho3bsy0adOIj4/Hy8uLRo0akZeXR926dYG/5tsfOnRopceeP4bJYrFw1VVXAXDs2DH69+/P7t27ad++PXfdZR1jVa9ePftgv2nTpvHLL79w9913X3RBF6WUqq6q+jzlSimlCnBxcWHChAm88847jB49mm7durFs2TLAsUtH8+bNadSoERs3buTVV18lICAAqPz59vPy8uyX87vYNG/enPj4eHr16sUTTzxBgwYN6N+/P0uXLrX3jT98+DAiQv369WnYsCE333yzJuRKqRpNk3KllKpGunTpwvDhw+3JbteuXS+YYgysrdHff/899913H2PHjuWOO+6o1Djz8vL45z//yT//+U9WrlzpUDZq1Chee+01mjdvzqZNm3jxxReZMmUKr7/+uj1xDwwMpH///pUas7NZLBZOnz7Nyy+/7PBlRilVO2hSrpRS1UjdunXx9PS0D/Bcvnw5LVu2BP5qKd+4cSMWi4X+/fsTGxvL3XffXakxrl27li5dupCSkkJISAjPPvuswzzbIkJaWhrNmjWzzyN+//33k5WVxalTf82hP2rUqEqN29lcXFxo0KABO3fu5PDhw84ORylVybRPuVJKlaOEhARatGhR4beTl5eHiJCYmMiAAQMA2Lt3L7GxsZw5c4YuXbpQr95Fp+etEPldbPK/DOzcuZNly5bZ+727uLjQrFkzDh48yPvvv0+7du349NNP6dy5Mw0aNHBKzM40Y8YMPD096d69O127duWyyy4jKCjI2WEppSqZtpQrpVQ5McYwZsyYC1bXqwguLi7k5OTQuHFjoqOjufnmm/nPf/7Dtddey1133UWdOnUqPIaiFNfFpmC/9+nTpxMYGMiCBQsYPXo0b7zxRqX3eXe2+fPns2TJEjp27Mi4ceMASEpK4ujRo6xevZrXXnuNFStWODlKpVRlqF3vfkopVQH27dtH06ZN8fHxoUePHri7u1f44jYiwvbt2/n888+Ji4vjvvvuY/To0RV2e6WRP9NLvuXLl9tX4izYxaZLly506NCh1q33kJWVZV9wzs3NjTFjxtC/f39WrVrF0qVLCQoKwtvbm7lz53LPPffw7rvvkpKSwogRI3TRJKVqMG0pV0qpS7R3716uvPJKXnrpJcaPHw/A5s2b8fPzQ0Q4ePAgGRkZwF/TAZangIAApk+fzqpVq6pMQl5QXl4eFovlgi42P/zwA4cOHQIq5nGpqtLT0xk7dizPPfecfbrHRo0acfToUYYMGcJvv/1Gr1692LVrF1u2bKFTp05cf/31PPLII8TFxQG6aJJSNZkm5TVEQkKCs0NQqtZJS0tjyJAhzJs3j/T0dFatWkXfvn2Jj49n0qRJvPTSS0yYMIHo6Gj7apvlKSAggMmTJ9tbXZ2psPeg4rrY3HnnndSpU6fWJJlZWVlMnTqV9evXc+7cOVavXg1A7969mTBhAvfeey//+9//qFu3LmFhYXTs2BGLxcLYsWO599576dixo5PvgVKqomn3lRogvx/rq6++Svv27Z0djlI1Vnp6Oq+88gq9evWiU6dOGGNwd3dn1apV+Pv74+3tzfr16xk4cCDNmzfnn//8J7/++iuvv/46H3/8cYUkoFVhVeGi3oOqchebyubh4cE//vEPnn76ab788ku2b99O586dCQkJwd3dndtuu82+765du8jMzOSpp55i/fr1jBs3TpNypWoBTcqrMWf0Y1Wqttq0aROPP/44V199Nb/99huvv/46X375Jenp6Xz00Ud4enoSGRnJNddcw4EDBzh58iSrV69m3759tGvXzj7rSHlz5qrCJXkPyu9i8+STT1aJFv3KkpeXZ5+2EqxfUFq2bIm7uzvXXXcdH374IWvWrCE4ONj+eOXl5XHu3DmaNGlCs2bNAOjZs6dT4ldKVT7tvlINObsfq1K10ZEjR7juuut4++23mTx5MsePH2fq1Kn07t2b999/n/feew+AqKgo2rVrxx133MH27dtZunQpt99+e4Uk5M5SmvegFi1aVJkuNpWhuEWT3N3dAQgNDaVDhw788ccfbNmyBbC+V7u6uuLr60u/fv3s+yqlag9tKa9G4uPjCQgIIDU1lSFDhjB9+nRGjhzp0I/11Vdf5cSJE9SpU4exY8cSFhamredKlVJh58yePXvw9vYmJSXFvuz7Dz/8wK5du+jQoQPZ2dl4eHjQoUMHkpOT6dixI+3bt69R596lvgeNeWgcyWfSynTbZe1iUxlOnjhOly5d6NatGxERETz77LN4enrSq1cv+z75r63+/ftz7NgxtmzZwurVq7nmmmvo27cvAEOHDnXWXVBKOZEm5dVAeno6L774Iv/3f//HunXrcHd3x93dnZ9//tlp/ViVupj8BK66fSm0WCyAtbuBxWKxL/t+880389JLL/Hiiy/i4eFBQkICl19+OXPmzOHNN9/E3d2d7Oxs/vjjD/sUf9XpfhenrO9ByWfSnNbFpjIJUuSiSfnnQf5rIiAggMzMTKZMmUKLFi3429/+5szQlVJVQM35PbWGmjt3Ll27diUnJ4eHHnqIqKgorr76avr27cu8efPIzMwstB/rzp077f1Yq4r4+HhAu9TUFn//+9/5888/q1VieujgfgICAnj++b8SwPz4u3TpwosvvkhYWBhgPTdfe+01jhw5QmZmJsYYPDw8mDt3Lo0bN3ZK/BWhJr0HVbQGfo2KXDTpfFFRUXzwwQe88cYb7Ny5k06dOlVipEqpqkhbyquwAwcOEB8fz5dffkloaCj//ve/ycrKwsXFhd69e9O1a1d7P82oqCjuvPNOmjZtyoIFC1i/fj3vvfdelerH+ve//52PPvqIK664wtmhqApy9OhRGjdujIeHB3379qVhw4YVNsCxvGWmn+No/BGeeeYZ5s2bxz333EObNm0c4u/YsaPDLBhLliyhW7duDqtn1qS+0zXtPaiiubm5OTz/RS2aFB4eTnh4OMeOHXNKnEqpqqn2vFtWA+e3ILdu3ZopU6YQGhoKgL+/P59//rm93NPT094Kk9+PNTQ0lOeff55Vq1bRpk2bygu+CEePHiU7OxvAIUlTNcvvv/9O69atef7555k6dSoAv/zyC66urri4uNh/JanK6tStR6cuV/P444/Tv39/pkyZAmBPKvPPz6ysLA4fPsygQYP46KOPuPrqq50Wc3mrie9BzlDcoklxcXHV6tcjpVTl0aS8irBYLPYPxPOT1vzrN954I5dffjm///67vczNzc3ejzV/Cq2q8IZfE5I0VXLx8fE88sgjvP/++yQkJPDTTz/Rp08fzp07x+OPP86UKVOYMmUKf/zxB1B1uzDVrecNwPjx49m/fz8rVqwArElW/nnl6elJRkYG1113HVFRUfbBedVdTXsPcqbiFk266667HH5ZUUqpfJqUVwGF9WMtKL+lLi0tDU9PT+rWrWsvs1gsVbIfa01J0lTh0tPTee655/jmm29ITEwkOzubc+fOsX37dvz8/GjZsiUbN24kJSWF1q1bM3v2bK666io+/PBDoOonbU2bNmX06NFMnz4dAFdXV/bu3csbb7zBgQMHaNu2LY899piToyw/NfE9qDCVNa6l4KJJ//nPfxgyZAjvvfcejRo1qtDbVUpVb5qUO1nBfqw//vgj+/fvx8XFpdAuHm3btuXIkSOsWrUKsH6w5H9YOrsfa01P0tRfPvnkEyIjIzl58iRRUVHcfPPNDBw4kDZt2jB9+nQyMjJo06YN3bp148yZM+zcuZN9+/axZ88eezeIqi5/efPLLruMxx57jIkTJ7Jv3z4GDRpE69atnR1euaop70ElUZmDj/MXTVq1alWtXcVUqapORFqKyGoR2S0iMSLyuDPjcXpSXtQDIiJTRSRBRHbY/gYUOGayiOwXkb0iUq3nkSppP9b8fps333wzcXFxQOUlshfrA14bkjRllZKSQkZGBp999hnvvvsuU6dOJSgoiPT0dEaOHMncuXN59913cXV1Zf369bRr144hQ4Ywb948li9fTp8+fZx9F0rExcWF9PR0Tpw4wRdffMHll1/OLbfcQmBgoLNDK3fV4T2oLJw1riUgIKBWLZqkVDWVC0wwxrQHugKPiEh7ZwVTFWZfyX9AokTEB9gmIvnLoL1ujJlZcGfbg3UHEAo0B34SkRBjTF6lRl2OCvZjve2221ixYgX9+/d3WKbZzc36VA0bNox27dpVaDzGGJKTk5k9ezbPPfdcsbMnFEzSwsLCyM7OZt++ffYk7eabb8bX1xeA9evX88gjj9iTtM2bN/PJJ59U6H1RZXP+HOMNGzZk9OjRuLm5cfz4cQYNGoSbmxtvvfUWTz75JL6+vuTk5ODu7m6fRm/AgAFcf/31eHh4OPGelN7s2bMJDw9n5cqVNT6xqmrvQeXh999/Z9CgQVx33XVcdtllzJgxg19++YUnn3zSPq4lICDA2WEqpZzIGHMMOGa7nCYifwAtgN3OiMfpSXkxD0hRBgJfGWOygDgR2Q9cA2yq8GArWMF+rP3797f3Y122bBk33ngjbdu2rZQPQxHB1dWVM2fOkJOTYx+cCbU7Sattzl9EJ/814ObmRl5eHps2bWLkyJEMGzaMSZMm8fjjj/P222/j7u5ORkYGv/76K48/bv0lsDo+1/nJW21SVd6DykP+uJYnn3ySe+65x2Fcyz//+U/S0tIICAhg5MiRXHnllRXSz3zsw4+SdDq1THVUh5VMlaoJRCQQ6AxsdlYMTk/KCzrvAekOjBORvwNbsbamp2BN2H8tcFg8RSTxIjIGGAPg7u7OmjVrHMqbNm1KUFAQsbGxhIaGsm7dugvq6NatG3FxcQQEBJCYmMiRI0ccytu3C4a8bNwyksit1wSP0wcuqCO7QWvczp0g16sRrlmncc067VDeLrg1Z8+e5fDhw9x777188cUXDBkyBHd3dzp16kRQUBDBwcH8/vvvhISEEBcXx/Hjxx3qCAwMxM/Pj8TERAICAti82fE1JSL07t2b6OhoQkNDiY2N5cSJEw77rFy5ko4dO9KqVSvy8vLYs2cPGzdutJe7uLjQo0cPtm/fzlVXXcUff/xBUlISYJ2d4pdffuGmm25i6NChTJkyhYceeog777wTsE4jlz+n8w033MDvv/9Oly5diI6OJiUlBYABN/TFIyWW3Lr+IC5gycG41sH9rONMLcbFnZz6Qbin/kmO7xW4px5G8jIZcENf+3Pcvn17Apo3wyUzBePijvu5o451uHraj83xvRz31D+RvCyHfXLqNeeKli04duwYrq6u7N692x6jtY469mNzfK/A/UwcYslxrMM7AMnLBBd3MBbc0hMd4mzYsCHAX3WcPogYx8VGcnxa4pKTjsXVE7Hk4JZx0qE8olNHLBYLO3bsIDw8nP59e9pj/KuOK3DJTsXiXg/Jy8Qt45RDucXdh9x6TXA7e4xcnwB86rjSpEkTBgwYwP3334/FYqFbt27Ex8fj7+9PcnIyvr6+hIWFERsby2233caDDz7Iv/71L06dOkVoaCjjx49n+/btDrdT8HxqF9z6gjjzPBuQ59mgROdTXS8v9u3bR0JCAvDX6yfPsyEWD19cslLI82qMx5mDhdQRjNu5o+TW9cc14xSu2an2OtasWVMu51OrVq3w8fEhOTkZf39/tm7d6lDu5uZGjx49iIqKolOnTsTExNjPp3zBwcF4enpy9uxZ/Pz8iIqKcijPb8HPf/24pcXjkpvusE9JzieAbdu20blzZ66++uoL3oNuuOEGfH19SUhIwNPTk127djnU4e3tTUREBNu2baNLly5s3bqVs2fP2ssH3NAXyT5rPT/EBcQFt3OOc3Rf7HwacENfUlJSSEtLo06dOuTl5bF37157eUZGBt999x2DBw/Gy8uLzMxM9u7dy5w5c0hPT+fo0aMsWrSI66+/ngYNGvDUU0/x448/MnXqVB566CH7bbidTSC3XnPc046Q43s5Hqf3g3Hs7lLU+ZT/+mnSpAkhISG0bt2K67oNxiNlH+CY9Gf7BuKadRqLhw8uOem4Zjo+93kevuR5NSbul/9hsVgu6fOpRYsWBAQEEBcXR0hICBs2bLigjh49ehAbG0tQUBDx8fH28ylfy5Yt8ff3Jz4+nqCgIDZturD9q1evXsTExFTo51N5n08N6vte8P5jxI2cBq1KfD418mvokFcMuKEv7mfiCv18cqijXjPra8pYHD6f8l8/FzufwDoFaf6aAfmfTwX5+voSHh5ur2Pz5s1kZGQ4fIYV9vlUkMWtLrk+ARd8PhX8DAsPDyc5ORlvb2+ysrLYt2+fQx2NGjUiNDTU/vm0YcOGCxbUioiIIDExET8/P9LS0jh40PE9O/98iomJISwsjLVr117wJToyMtLh8+nQoUP5RW4iUvDFMscYM4fziIg38C0w3hhTtm/SZSBVZdYL2wOyFphujPlORPyBU1jfyaYBzYwx94vIW8CvxpjPbMd9CCw1xnxTXP316tUz586dK/e4bx91T7ksH/3NF/MA64DJG2+8kd27dzNlypRKm+Hh1VdfZcmSJdxxxx3s3LmTadOm8dprrzF9+nSWLVvGd999x8KFCxkzZgzTpk0rdEGYgj91Hz16lKuuuorY2Fj8/PwAyMzMLHYqsLI+lgUfx/Kor7rUWd71ZaafY/K9N/HMUxOYN28e8+fPv2ARnfOtXLmSr7/+mnfeeQd3d/cS3U5Vu9+F1VnWls7ybuU8/5cqqB7vQRXx3BQUGxvLvffeS7t27fDz8yM6OpqvvvqKFStW8N133+Hn58fs2bP5v//7P/r168ecOXN4/PHHWbhwIS1atOC+++4rlzgr4zWpykd1eC+vCLXtNSki6caYehfZxx1YDCw3xvynciIrXJVoKbc9IN8CnxtjvgMwxiQWKH8f6wMGkAC0LHB4gG1bjVCZ/VgTEhLw9vamfv36HD9+nE8++YSWLVvy8ssvs2HDBpKSkkhJSWHu3Lns27ePK6+8kvnz5xe60iFgT8gBYmJiGDRoED4+PvZtOjdv9VBw4N+xY8eYMmUKX3zxxQVdmNLT09m/fz//+c9/2LNnD88880yJE/LqIul0apk/ZMvCYrFw+vRp3nrrLaZMmVIpAyurY1/65ORk/Pz8mDt3LgC33HILH374IRMmTGDw4MH29x4d16KUKkisb6ofAn84OyGHKpCUF/WAiEgzW39zgMFA/u+li4AvROQ/WAd6BgO/VWLIFaoy+rFmZGQwZcoUfvzxR9q0acOYMWPo0aMHM2fOJDk5mYyMDB588EE2btzIsmXLuO6665g5cyYrV65k5cqVtTpJqy1KMvCvbt26bNmyhSuvvJKPP/7YidHWXC4uLjRs2JDDhw+TlJRUKfNcV/W+9IX9WpCbm0tgYCB//vknV1xxBdOnT2fSpElcf/31hIeH67gWpVRRugN3A7+LyA7btn8aY5Y4IxinJ+UU8YAAI0WkE9buK4eAsQDGmBgRmY91ZGwu8Eh1nnnlfC4uLhX+k/mMGTM4duwYMTExLFq0iJkzZ7JmzRo6dOjAzz//bJ/LNzMzk5tuuom7776bEydO8OGHH/L+++/zr3/9S5O0WqKogX9Llixh6NCh3H///dViWrzq5t///jfNmzcnMjISPz8/XFxcKm3hmcp4D7pURQ0+btSoEceOHePYsWO0bNmSsLAw2rVrx9y5cwkPD68xg49rg+K6ySlV3owxG4Aq8yHm9KS8mAekyG8pxpjpwPQKC8rJKvIn86ysLM6dO8fAgQMREU6ePEnfvn05evQowcHBBAcHA9YBQiJCgwYNeOONN/jtt99YuHAhnTp1Ii4uTpO0WiJ/EZ2VK1fy2GOP4eHhQZ8+fRg8eDCXX365s8Orkd577z2WLFnC+PHjefzxx1m8eLH9l6gNGzYQFxdHREQEXbp0qbAYnN1tpzD5q46OHj2aadOmOZRdeeWVtG3blm+//ZaAgAACAgIYP348t956K6mpqfj6+uLl5cV3331Xq7vRFfYrg7OVZgpeVTpV8flWxdNXfylUlUGxpXH+KGdPT0/8/PxYvnw53bt3Z/r06aSmphIZGekws8OePXs4dco6o0Dr1q0ZOXIknTp1KnKlw/wkTd8AapbatIiOMx08eNA+I0VmZibTp09n6NChREZGsnz5curWrYvFYmHevHn4+/sza9YsfvjhB6B6vi+VVnGrjua3nuePgfj4449JSUlh//79dO3atVaPa7FYLCQnJ/Piiy8CFbPYU1lff+dPwVsZCzvVVJXxfFcH1fk9UZPyYlTnF3hubi4TJ05kwoQJ/PTTTw5lkyZNYtq0abRo0YIdO3bw2muvMXbsWCZOnGjfp0uXLgwYYF1EteALXJO02id/4F9CQkKlzQZUW6Snp/PUU08xePBgTp8+DUCzZs1YuXIlI0aMYMuWLVx33XWcPn2a1atXc8011zBo0CCGDh1qn+6tOr0vXariVh11cXEhNzcXf39/Jk6cSG5uLoMHD+ahhx7i+uuvrxWPT1HOH5NQHsrrc3HmzJksXryYkydP8ueff3LmzBnc3d0rpKW8OidppVERz3d1UJ1ztfM5vftKVeaMQVblwRjDY489RmpqKjfddBOvvPIKe/fu5YEHHsDT0xM3NzdOnTqFn58f3t7WAX1jx45l48aNpKWl4ePjg4uLC6NGjQIufIFXx9kZ1KWr6gP/qqujR49y1113ARAdHW3fPnz4cPr168eyZcu47bbbSE9Pp3HjxvTq1Yvly5czceJE5s+fz2effeas0J2iuMHH+auNdurUiU6dOrFz506uuuoqZ4brVBU5JqE8PhdnzpzJjz/+iI+PD1OnTmXatGk0adIEgGXLlpGUlET37t0vubHHGbMWOZMzx6CUVXmMIaiuuVphNCkvRHV+gQPk5uawY8cOli9fjo+PD40bN2bJkiUsWLDAngR06NCBjRs3MmvWLIKCgnjzzTe59tprHX7qLYomabVLVR74V515eXnxt7/9zd7FbMuWLWRnZxMUFETz5s3tC2/Vq1fPvpDKtGnT+OWXX7j77rsJCwtzWuzOVNyqo3/7299o165drU7I33///QoZk1DWz8UTJ07QpEkTjDEcO3asyCl4P/30U/r27cusWbO4/fbb6d69e2kfghqVpF1MRT3fFaU8xxBU91ytMJqUn6e6vcAL4+7uQWBgIB9//DGPPvoo3bt359ixY/zyyy9cd911NG/eHBFh9uzZrFu3jlmzZjF27FhGjhxZovrLI0kDTdSqk6o48K+6yc3NtbfognU11z59+vDBBx8QHByMv78/7du357fffuP7778nKCiInJwcEhISEBHq168PwM033+ysu1AlFDX4eODAgbW2G13BxDM9PZ2XXnrJvkLm+WMShg0bxqxZsxg2bBi33npriQYDluVz8dy5c0ydOpWff/6Znj17MnLkSLp27cprr71GUlLSBVPw9uzZkwceeIAPP/yQ9PT0QqIpWk1M0gpT1ufbmc4fQ+Dq6npJiXlJX5PVjSblVO8XeFEGDx7MsmXLOHbsGM2aNeOqq65i586dJCcn07x5c2JiYrj22mvp2bMnzz77bKnrL2uSBpqoqdohNzeXSZMmkZOTw6233sr1119vL+vUqRPdu3enXbt2TJgwAYCnn36aRx99lMWLF+Pu7k5gYCD9+/d3VvhVTsFxLWvWrGHKlCnccsstzg7LKc6dO8eUKVPYsGED1113HcOGDaNz584sXbqU2bNnk5qayqRJk/j2228dxiSIyEXHJJRHon/gwAEef/xxgoKC+P7771m0aBGvvPIK3333HZ06dWL16tXcfffdRU7B+/3335f4sagJDWoXU5HPd0WbOXMm7dq1IzIykqNHj9rHEJTGpbwmAVewroljqmrCVkCt7oNw7tw5JkyYwIABA5g8eTJRUVF07tyZZcuWVftBVj169KBx48b2+cLDw8P57bffyMjIYNGiRWzZskVHuStVwfLHdxw/fpxrrrmGV155hbfffpusrCzAOhvS7bffbk/IAW677TauuOIKcnNz7edo/vgOZaWDj+HAgQMMGTIEi8XCRx99RLNmzZg8eTK9evVi0qRJ3HbbbcyfP99hTMK+ffuYOHEijz76KK1atSq03vL8XGzUqBH/+c9/+O9//8vll19OWloa1157LZmZmQQHBzNmzBi8vLwumII3ODiYhQsXEh4eXuxjkN/1yxjDuXPnavSsRRX1fFeG/DEECQkJTJ06lZYtWzqMIfj88885dOhQkceX5TWJbcrt6pCQQy1OyqvzC7wkmjVrxsCBA1m6dCkLFizg0KFDeHh44OHhwa233sq9996rq20qVcHyx3e888473HnnnUycOJHY2FgWLFgAWBOD/MHWAFFRUUyePJl27drh5uamYzeK8OSTT/LGG2/U6oHm9erVY/To0bz++uu0b9+em2++mSZNmnDo0CHq16/PnXfeiY+PD35+fg5jEvr27csPP/xAr169LqizrJ+L569d0KBBA0JCQsjIyOCll17i7bffZt++fQwZMsQhCStqCt6ipKen89BDDzFnzhzOnj2LiNT4WYsq4vmuSPnPp8VisY8hGDt2LC1btrxgDEFGRgazZs1i48aNF9RT1tckUK1aH2tt95X8F/jw4cMBa4vVli1bOHToEIGBgTVikNW1117L5MmTWbBgAc899xzjxo2rFnErVVOUZHwHQGpqKi+//DJLly7l6aefLvH4jtqqNg4+Pn9MQtOmTe3T1oJ1fvs9e/Zc0K/+8OHDJR6TcKmfi+vXr+fkyZMsXLgQi8Vi76KV343Fy8uLAQMG8K9//QuwdtEaO3Ysy5cvBy6cgre4hPnMmTNMmjSJJUuWkJOTQ3h4OF27dmXEiBH069ePFStWcOutt1b7WYsq4/muCFlZWUyYMIHVq1czaNAghg8fzrXXXsvMmTNJTk4u9RiCsuZqnTp10qS8OqguL/Cyuummm+xz9RY8wZVSlaMk4ztCQ0MZPHgwM2bMcHa41UZtGXxssViYOHHiBWMSzv+VJTk52b4ic0HFjUkoj8TPGMOyZcvIy8uzd9EqOAVv/pR3BbuiDB48mNTUVHJycuy/CBU1Be/56tSpw0MPPcQrr7zC1KlT2bBhA61bt+ayyy6jcePG9nqqa4NaUWNQyuP5rgzz5s0jISGBdevW8cMPPzBkyBB+//13unTpwooVKy46hqC6fhkpLzX+t1ERcRORmdnZ2Q6L6FSXF3h5cHd314RcKSe52PiOzZs3k5eXx9VXX+3cQFWVY4whettvhY5JyE9e87vK7t69m/bt2wPw5Zdf8scff9jrOX9MQlGLy13K52JaWlqxXbTOT7K3b9/OpEmTaN++Pe7u7hdNwgtblbpt27b4+voyePBgdu7cyY4dOy44rmCS1rBhQ26++eYqn5AXNwalLM93ZcWeLzIykkaNGnHvvffStWtXnnnmGQIDA4sdQ/Dtt9/yxRdflMtrsjqr0ZmaWF/FbwK+Li4uF3yDh79+Kjv/Bd6pUyeuvPJKoGYMsqptP/UqVVXkj++YNGkSbdq04eqrr3YY31Hd+raqypORfpbTp1N45513Cl1zomBXj40bN+Ln58ftt9/OuXPnivySd7HF5fL3Kennoq+vb4m6aCUlJfHiiy+yfv16nnrqqYt20SquxTg/zp49e7Jy5UpWr15N27ZtHfq0V8ck7WJrjFzK812x8f7Vqp0fV2pqKsnJyfZYX3vtNbp168YjjzxCu3btgAvHELRq1YpHHnmk3F6T1VmNTsoBH6AT8Dc3N7c7J06cWKVf4BWptvzUq1RVpOM71KWoW8+HevXqXTThzc7OZteuXRhjeOaZZ7jjjjuKrDO/Zbs8E7+SdtG644478qepK9bFvjgYYzDG4OLiwl133cVrr73Grl27WLhwId27d7d3laluSVpJx6CU5vmuCEV1qQIYOXIkN954I6NGjaJTp040bdqUYcOGMWPGDD755BMAIiIiHMYQVMRrsrqq0d1XjDGpwCHgXoDu3bvTuXNnfvnlF44ePWp/kvNf4GvXruX2229n6dKltGnTxmlxK6Vqnptuuon33nuPXbt2MW7cOGeHo6qJ5gGXs2PHDo4dO4a3tzdXXXUVderUITk5GYCYmBg8PDyYMmUK27dvv2iCVrBlG8rnc7GkXbS6detWovt8sS4xgH1mopCQEESEUaNGMXfuXDw8PEp0G1XV4MGDy/X5Lm/FdakCaNGiBUOHDuXVV1/l+PHjAPTv359WrVphjMFisdifL7C2sFfEa7K6qtFJuc33QKf8fklV7QWulKo9dHyHKq1GlzUpNuH99ddfsVgsDBo0qMR1lnfid7EpeO+//35cXV1LHF9JkzRjDD/99BM//PADb731Ftu3b6dDhw4lvp2q6GJfcC7l+S5PBbtUFfWF6fnnn8fT05MXXniBDz74gKeffpr69esjIkVO81rVv4xUltqQlG8ATuUPFqlqL3CllFKqKF5edYtNeEePHl3q+ewrIvHL76K1dOlSbrzxRoYMGUJYWNglj5m4WJK2a9cuLBYL3bp1IyEhgbvuuuuSbqequdgXnEt5vstTwS5VcOEXJrC2fs+YMYObb76ZVatWMX78eJ544oli663qX0YqS41vsjHGHBORhXl5eU8vWLBAB1kppZymrAOuQQdd10blPSahogYfl+cUvD169GDr1q18/PHHTJ48mfDwcB577DFGjhzJokWLSEpKol27dtSrV69Mt1MVVfUxKAW7VBU1hiAkJIRbbrmFW265pUR16oB4qxqflAMYY36pU6cOS5curZIvcKVU7VDWAdegg65rq/Jec6KiEr/yWim6tidpVXmNkYJdqgr7wpScnExISMhFF4I6X1X/MlIZqtYzXYFcXV157733quQLXCmllLqY8kp481VU4leevwjV5iStvJ/v8pLfpaoivjBV5S8jlaHa3mMRuRGYBbgCHxhjXr7YMVX1Ba6UUkqVRHmvOVERn4vl+YtQbU7SqnJ3t4ps1a7sXO1S8smKUi1f4SLiCrwN3ADEA1tEZJExZvfFjtVFdJRSSlVXtXHNidraoFbVu7tVh19aLqYs+WRFqJZJOXANsN8YcxBARL4CBgIXfRBr4xuaUkopVZiq3BpbkDaoVU1V/ZeWErjkfLIiVNekvAVwpMD1eCDSSbEopZRS1VJVb43Npw1qVVc1/8JUpfJJMcY467YvmYjcDtxojHnAdv1uINIYM+68/cYAY2xXw4GMElTvBuSWY7gVUafGWHXrrA4xVkSdGmPVrbM6xFgRdWqMVbfO6hBjRdSpMVZ+nV5AVIHrc4wxc/KvlDSfrCzVtaU8AWhZ4HqAbZsD2wM/5/ztxRGRrcaYiLKFV7F1aoxVt87qEGNF1KkxVt06q0OMFVGnxlh166wOMVZEnRpjlayzRPlkZamuK3puAYJFJEhEPIA7gEVOjkkppZRSSlUfVSqfrJYt5caYXBEZByzHOoXNXGNMjJPDUkoppZRS1URVyyerZVIOYIxZAiypgKpL1d3FSXVqjFW3zuoQY0XUqTFW3TqrQ4wVUafGWHXrrA4xVkSdGmMVrLMC88lSq5YDPZVSSimllKpJqmufcqWUUkoppWoMTcptRORGEdkrIvtFZFI51DdXRE6IyK7yiM9WZ0sRWS0iu0UkRkQeL2N9dUTkNxGJttVXtslqHet2FZHtIrK4nOo7JCK/i8gOEdlaDvU1EJFvRGSPiPwhIt3KWF9bW2z5f6kiMr6MdT5he152iciXIlKnLPXZ6nzcVl/MpcZX2GtbRPxEZKWI7LP9b1gOdQ6zxWkRkVKNsi+ivldtz/dOEfleRBqUQ53TbPXtEJEVItK8LPUVKJsgIkZEGpdDjFNFJKHAa3NAWeu0bX/U9njGiMj/lTHGrwvEd0hEdpQ1RhHpJCK/5r9niMg15VBnmIhssr0X/SAivqWor9D370s9d4qpryznTVF1XvK5U0ydl3TuFFVfgfJSnzvFxHhJ505xMZbhvCkqxks+d4qp85LOnWLqK8t5U2ieItaBmZvFmrN9LdZBmtWfMabW/2Ht3H8AaAV4ANFA+zLW2Qvr3Oi7yjHOZkC47bIPEFuWOAEBvG2X3YHNQNdyivVJ4AtgcTnVdwhoXI6P5TzgAdtlD6BBOb+ejgNXlKGOFkAc4GW7Ph+4t4xxdQB2AXWxjif5CWhzCfVc8NoG/g+YZLs8CXilHOq8EmgLrAEiyqG+/oCb7fIr5RSjb4HLjwHvlqU+2/aWWAcd/Vna13wRMU4FJpbhdVNYnX1trx9P2/UmZb3fBcpfA6aUQ4wrgJtslwcAa8qhzi1Ab9vl+4Fppaiv0PfvSz13iqmvLOdNUXVe8rlTTJ2XdO4UVZ/t+iWdO8XEeEnnTjH1leW8uejnf2nPnWLivKRzp5j6ynLeFJqnYP1MvMO2/V3godI+T1XxT1vKrezLrBpjsoH8ZVYvmTFmHZBcHsEVqPOYMSbKdjkN+ANr8nap9RljzFnbVXfbX5kHGYhIAHAz8EFZ66oIIlIf6wfuhwDGmGxjzOlyvIl+wAFjzJ9lrMcN8BIRN6yJ9NEy1nclsNkYk26MyQXWAkNKW0kRr+2BWL/oYPs/qKx1GmP+MMbsLW18xdS3wna/AX7FOh9tWessuIxdPUpx/hTzHvE68HRp6ipBnZesiDofAl42xmTZ9jlRxvoAEBEBhgNflkOMBshvkatPKc+fIuoMAdbZLq8EhpaivqLevy/p3CmqvjKeN0XVecnnTjF1XtK5c5HPwUs6dyrgs7Wo+spy3hQb46WcO8XUeUnnTjH1leW8KSpPuQ74xra91J85VZUm5VaFLbN6ySdkZRCRQKAz1m+NZanH1fZz1wlgpTGmTPXZvIH1jdFSDnXlM8AKEdkm1pVayyIIOAl8JNYuNh+ISL2yh2h3B6VMKs5njEkAZgKHgWPAGWPMijLGtQvoKSKNRKQu1haQlhc5pqT8jTHHbJePA/7lVG9FuR9YWh4Vich0ETkC3AlMKWNdA4EEY0x0ecRWwDhbV4G5Je0ecREhWF9Lm0VkrYhcXQ51AvQEEo0x+8qhrvHAq7bnZibw/+3da6wdVRXA8f/SQoLVIBgJNSA1ja/EkGqFoDZI2qrUkBqMb43WV0TRAImSQP3ityaKSELsB9PYpBQjSCn4wUd8YDRRKi0UStvUGqpebSuaauITAssPe994cj3ncO/sac+99v9LTs45995Zd8+Zs2bWzN4zc0MPMR/lvwds3knH/Jmx/m7Onb62B7OM2Tl3ZsZszZ3BeH3lzpD5bsqdGfF6yZsRy6Ypd2bEvJbG3JkRrylvZtYplJENfxnYUZz3NdtsWZQvQBHxXOAu4NoZRxvmLDOfyszllCMfF0fEqxrbdgXwx8zc1RJniJWZ+RpgLXB1RFzaEGsRpVt6U2a+Gvg7pdu4WR3Xtg64szHOWZSV2EuAFwGLI+IDLTEzcz+l6/n7wHeBh4CnWmKO+D9JDz0uJ0pEbKDcnnlbH/Eyc0Nmnl/jdb41c91RupHGwn6ITcAyYDllB++mHmIuAs6mdCN/DrijHqlr9V4ad2gHfBK4ri6b66g9Y40+AnwqInZRuuefmGuAcevvLrnT5/bgmWK25M6wmC25Mxivtqk5d4a0sSl3hsRrzpsxy7tz7gyJ2ZQ7Q+I15c3MOgV4xVymX0gsyot5dZvVcSLiNMqXfVtmbu8rbh2+8WPg8sZQbwDWRcRhyjCgVRFxW2PM6SPH0919d1MSs6spYGqgV+BblCK9D2uB3Zl5rDHOGuCxzHw8M58EtgOvb21cZm7OzBWZeSlwnDLmrw/HImIJQH2edbfsyRQR64ErgPfXAqhP25hDt+wQyyg7YXtq/pwH7I6Ic1salZnH6kbtaeBrtOXOtClge+1a3knpFZvTSakz1WFabwe+2UP7AD5EyRsoO8nN852ZBzLzzZm5glIA/Xou049Yf3fOnROxPRgVsyV3ZtHOOeXOkHjNuTOsjS25M2Kem/JmzLLpnDsjYnbOnRGfY1PeTBuoU14HPL/ON8zjmm2uLMqLeXWb1VHqHvVmYH9mfrmHeC+MehZ9RJwBvAk40BIzM2/IzPMycynlc/xRZjYd4Y2IxRHxvOnXlJOOOl/VJjOPAr+LiJfXH60G9rW0cUBfR/p+C1wSEc+py301ZXxek4g4pz6/mLISv701ZnUvZUVOfb6np7i9iYjLKcOq1mXmP3qK+dKBt2+jIX8y85HMPCczl9b8maKcNHW0sY1LBt5eSUPuDNhBOWmNiHgZ5WTpPzXGXAMcyMypxjjT/gC8sb5eBTQPiRnIn2cBn6ecYDbbaUetvzvlTt/bg3ExW3JnTMxOuTMsXmvujGljp9wZs2x20DFvnmF5d8qdMTE75c6Yz7Elb4bVKfspxfk76p/Ny21OJzkPzjadDw/K+NqDlD24DT3E+walu+tJygrioz3EXEnp2nyYMvTgIeCtDfEuBB6s8fYyxysezCL+ZfRw9RXKVXH21MejPS2f5cADdd53AGf1EHMx8GfgzJ4+vy9QNlR7ga3UM/YbY/6UsgOyB1jdMcb/fLeBFwA/pKy8fwCc3UPMK+vrfwPHgO81xjtEOXdkOndmfaWUMTHvqsvnYeDblBPYOseb8fvDzP3qK8PauBV4pLbxXmBJDzFPB26r874bWNU638AW4Koev5MrgV31u34/sKKHmNdQthMHgY1QbsA3y3hD199dc2dMvJa8GRWzc+6Midkpd0bFa8mdMW3slDtj4rXkzcj5pmPujGlnp9wZE68lb4bWKZS6YGf9bt5JD9vH+fDwjp6SJEnShDl8RZIkSZowi3JJkiRpwizKJUmSpAmzKJckSZImzKJckiRJmjCLckmSJGnCLMolaQGKiPsi4tZJt0OS1A+LckmSJGnCLMolaYGJiC2U22BfHRFZH8siYnNEPBYR/4yIX0XE9fXW1tPTLYqImyPieH3cHBGbIuK+Sc2LJKmwKJekheca4OfA14El9TEF/B54F/BKYANwI/Dhgek+C6wHPgZcQtkGvO9kNVqSNFpk5qTbIEmao3p0e29mfnrM32wEXpuZa+r7I8Atmbmxvg/gAHAkMy874Y2WJI20aNINkCT1IyKuohwFvwA4AzgN+E393ZnAucDO6b/PzIyIncD5J7+1kqRBDl+RpP8DEfFu4CvAFuAtwHLgq8DpE2uUJGnWLMolaWF6Anj2wPuVwP2ZeWtm7s7MQ8Cy6V9m5l+Bo8BF0z+rw1cuQpI0cQ5fkaSF6TBwcUQsBf4GHALWR8Ta+vo9lCu0HB+Y5hbg+og4COwDPkE5SfTIyWu2JGkYj5RL0sL0JcrR8n3A48B3gDuA24FfAkuBm4ZMs5Vy1ZZf1J/dDfzrxDdXkjSOV1+RpFNYRDwI/CwzPzPptkjSqczhK5J0ioiICygngf6EcmWWjwMX1mdJ0gRZlEvSqeNp4IPAFynDF/cBazPzgYm2SpLk8BVJkiRp0jzRU5IkSZowi3JJkiRpwizKJUmSpAmzKJckSZImzKJckiRJmjCLckmSJGnC/gPIoKnm22wVNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import collections\n",
    "import itertools\n",
    "\n",
    "cnt = item_categories.feat.map(collections.Counter)\n",
    "cnt_all = collections.Counter()\n",
    "for d in cnt:\n",
    "    cnt_all.update(d)\n",
    "# print(dict(cnt_all))\n",
    "all_feat = pd.Series(sorted(list(itertools.chain.from_iterable([[i]*k for i,k in cnt_all.items()]))),name=\"feat\")\n",
    "# print(all_feat)\n",
    "visual_statistics_discrete(all_feat, \"tag\", size=(12,4.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e17b95f-1622-4562-b0bb-9805203894a3",
   "metadata": {},
   "source": [
    "## Distribution of watch_ratio in big matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "43a8d258-c905-4a0d-a0d8-e3457a78813f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1.241552e+07\n",
      "mean     8.510664e-01\n",
      "std      7.185296e-01\n",
      "min      0.000000e+00\n",
      "25%      3.111008e-01\n",
      "50%      7.161066e-01\n",
      "75%      1.161843e+00\n",
      "max      5.000000e+00\n",
      "Name: watch_ratio, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "big_watch_ratio = big_matrix.watch_ratio[big_matrix.watch_ratio <= 5]\n",
    "print(big_watch_ratio.describe())\n",
    "visual_continue(big_watch_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e7702bc-222f-4df9-911f-a1bddf283b01",
   "metadata": {},
   "source": [
    "## Distribution of watch_ratio in small matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "23e003c8-650c-4b8c-9183-774c9b8160af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    4.653780e+06\n",
      "mean     8.553870e-01\n",
      "std      5.952060e-01\n",
      "min      0.000000e+00\n",
      "25%      4.657859e-01\n",
      "50%      7.662410e-01\n",
      "75%      1.114060e+00\n",
      "max      5.000000e+00\n",
      "Name: watch_ratio, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "small_watch_ratio = small_matrix.watch_ratio[small_matrix.watch_ratio <= 5]\n",
    "print(small_watch_ratio.describe())\n",
    "visual_continue(small_watch_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6b741b-5157-4c99-8ebc-ccdd5f544442",
   "metadata": {},
   "source": [
    "## Distribution of video duration in the big matrix (in millisecond)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "424bad61-72b9-413f-b210-ba62e790eedc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1.253081e+07\n",
      "mean     1.462157e+04\n",
      "std      1.983474e+04\n",
      "min      1.400000e+02\n",
      "25%      7.434000e+03\n",
      "50%      9.636000e+03\n",
      "75%      1.217900e+04\n",
      "max      3.150720e+05\n",
      "Name: video_duration, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "big_video_duration = big_matrix.video_duration\n",
    "print(big_video_duration.describe())\n",
    "# visual_continue(big_video_duration)\n",
    "visual_continue(big_video_duration[big_video_duration < 100000])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ab988e-2c62-44e8-8451-092cc3127a06",
   "metadata": {},
   "source": [
    "## Distribution of video duration in the small matrix (in millisecond)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cba36b8f-0daf-4b17-a4d5-2f7cd0ec516c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    4.676570e+06\n",
      "mean     1.448645e+04\n",
      "std      2.046711e+04\n",
      "min      3.067000e+03\n",
      "25%      7.523000e+03\n",
      "50%      9.600000e+03\n",
      "75%      1.193400e+04\n",
      "max      3.150720e+05\n",
      "Name: video_duration, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "small_video_duration = small_matrix.video_duration\n",
    "print(small_video_duration.describe())\n",
    "# visual_continue(small_video_duration)\n",
    "visual_continue(small_video_duration[small_video_duration < 100000])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c305f1-eba3-45db-b9c8-d54ebba1111b",
   "metadata": {},
   "source": [
    "## Distribution of each user's total play times in the big matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1da7528c-c955-4956-b7fa-aa7f54e92880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               date\n",
      "count   7176.000000\n",
      "mean    1746.210424\n",
      "std      991.832222\n",
      "min      100.000000\n",
      "25%      883.000000\n",
      "50%     1846.500000\n",
      "75%     2461.000000\n",
      "max    16015.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "big_play_time = big_matrix.groupby('user_id').agg({\"date\":len})\n",
    "big_play_time.name = \"play times\"\n",
    "print(big_play_time.describe())\n",
    "visual_continue(big_play_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95cf779b-ac7e-4dcc-a9c3-2186d0d364c8",
   "metadata": {},
   "source": [
    "## Distribution of each user's total play times in the small matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ee0e940c-cd61-4478-a639-e847a1ffcda5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              date\n",
      "count  1411.000000\n",
      "mean   3314.365698\n",
      "std       6.984852\n",
      "min    3295.000000\n",
      "25%    3309.000000\n",
      "50%    3315.000000\n",
      "75%    3320.000000\n",
      "max    3327.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "small_play_time = small_matrix.groupby('user_id').agg({\"date\":len})\n",
    "small_play_time.name = \"play times\"\n",
    "print(small_play_time.describe())\n",
    "visual_continue(small_play_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6d3118-d5ac-4215-bffa-c92b607930b2",
   "metadata": {},
   "source": [
    "## Distribution of each user's daily play times in the big matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eed8cc1e-8e42-4056-b2cf-dcb3f5c73b14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    188322.000000\n",
      "mean         66.539257\n",
      "std          78.752240\n",
      "min           1.000000\n",
      "25%          19.000000\n",
      "50%          42.000000\n",
      "75%          83.000000\n",
      "max        3268.000000\n",
      "Name: play times, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "big_daily_play_time = big_matrix.groupby(['user_id', 'date']).size()\n",
    "big_daily_play_time.name = \"play times\"\n",
    "print(big_daily_play_time.describe())\n",
    "visual_continue(big_daily_play_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c7094c-6e7f-4502-aebd-f9fdc9795092",
   "metadata": {},
   "source": [
    "## Distribution of each user's daily play times in the small matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f4219726-bc1a-4c95-8514-bbfa026f9576",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    86671.000000\n",
      "mean        51.857922\n",
      "std         32.608372\n",
      "min          1.000000\n",
      "25%         28.000000\n",
      "50%         47.000000\n",
      "75%         70.000000\n",
      "max        402.000000\n",
      "Name: play times, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "small_daily_play_time = small_matrix.groupby(['user_id', 'date']).size()\n",
    "small_daily_play_time.name = \"play times\"\n",
    "print(small_daily_play_time.describe())\n",
    "visual_continue(small_daily_play_time)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
